short-range positioning systems: fundamentals and advanced ... · • new context aware...
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Short-Range Positioning Systems: Fundamentals and Advanced Research
Results with Case Studies
Davide Dardari and Andrea Conti (*)
University of Bologna, Engineering Faculty, March 5-6, 2009
WiLAB –University of Bologna, ItalyDepartment of Electronics, Computer Science and Systems
(*) also with ENDIF, University of Ferrara, Italy
Short Course for Doctoral Students
([email protected], [email protected])
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Summary (1/2)
• Introduction– Motivations
– Localization basics
• First part (D. Dardari): Distance estimation (ranging)– Basic concepts on estimation theory
– Performance limits in Time-of-Arrival (TOA) estimation
– Ultra-wide bandwidth (UWB) signals
– Ranging with UWB signals
– Main sources of error in TOA estimation
– Practical TOA estimators
– The IEEE 802.15.4a standard
– Advanced issues
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Summary (2/2)
• Second part (A. Conti): Localization– Performance limits
– Localization schemes
– Cooperative localization
– Tracking
– Experimental results
• In addition:– Case studies
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Why localization is important
• new context aware applications (e.g., real-time location systems, RTLS)• new market opportunities ($6 Billion in 2017*)
[VICOM Project www.vicom-project.com]
Some examples:• inventory/people tracking (RFID)• surveillance/security• wireless sensor networks (WSN)• virtual immersive and augmented reality applications
* R. Das and P. Harrop ”RFID Forecast, Players and Opportunities 2007-2017”, 2007, http://www.idtechex.com.
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For example in WSNs……
Sensed data without position and time information is often meaningless
For example, in habitat environments monitored sensed events must be ordered both in time and space to permit a correct interpretation.
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In addition…
Positioning and time synchronization are also essential for basic mechanisms composing the network to work efficiently:
•MAC scheduling algorithms can reduce packet collisions•power-saving strategies (wake-up sleeping times)•networking protocols to improve performance of routing algorithms (geo-routing)•enabling interference avoidance techniques in future cognitive radios
In many schemes proper time synchronization is required to achieve high ranging accuracies in positioning techniques
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Some technologies enabling localization
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(c) VICom Project 2002-06(c) VICom Project 2002-06Location Provider
Plu
g IN
Plu
g IN
Plu
g IN
Plu
g IN
…
5-50 m5-10 m0.5 - 5 m5 cm - 50 cm beyond 30 m
GPSWLANMOTECRICKET/RF-ID Cellular
LFIntegration of different technologiesdepending on the environment
In addition, UWB-based localization (now practical)
High nodes’ density requiredHigh nodes’ density required
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• high localization accuracy (<1m) in harsh environments (e.g., indoor)
• low device complexity (low cost, low size)
• standards (e.g., IEEE802.15.4a)
Needs
But in many cases:
• nodes are GPS-denied (e.g., indoor, urban canyon)
• GPS is too expensive (e.g., WSN) and only a small fraction of nodes (anchors) have positioning information due to cost, size and power constraints
• higher accuracy than GPS is required
• no infrastructure available (anchor-free), only relative coordinates are estimated (ah hoc networks)
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Localization basics
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Localization Basics
The purpose of any localization algorithm is, given a set of measurements, to find the locations of target nodes with unknown positions.
Positioning occurs in two main steps:
1. Selected measurements are conducted between nodes2. Measurements are combined to determine the locations
of target nodes (localization algorithm).
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Ingredients for localization-aware applications
AlgorithmsAlgorithms
TechnologiesTechnologies
Position‐Based ApplicationsPosition‐Based Applications
RSSI, TOA, TDOA, AOA, …
Robustw/o memoryRange-, angle-, proximity-basedw/o cooperation
Error sourcesUncertainty
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Position estimation techniques classification (1/2)
•Anchor-based - Using GPS or using predefined coordinates, a subset of nodes in the network know their position a priori (these nodes are typically named anchors or beacons). Nodes with unknown positions (targets) use positioning information from anchors to determine their location.
Ranging between anchors and other nodes can be obtained by:- direct interaction (Single-Hop), - indirectly by means of intermediate nodes (Multi-Hop).
•Anchor-free - No nodes in the network have knowledge of their position a priori. Only relative coordinates can be found in such networks.
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•Range-based - Measurements provide distance information among nodes (*)
•Angle-based - Measurements provide angle information among nodes
•Range-free - Only connectivity information is used (*)
Position estimation techniques classification (2/2)
Other techniques
Interferometric – (low complexity, problems with multipath) (*)•Scene analysis – (e.g., based on receiver power “signature”)•Inertial – (error accumulation)•DC magnetic tracker – (accurate but expensive and low range)•Optical – (laser ranging systems, very accurate but expensive)•Hybrid – (e.g., Range-free and Inertial) (*)
(*) suitable for low cost devices (e.g., WSNs)
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Basic Concepts on
Estimation Theory
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Problem statement
Estimation theory basics
To obtain an estimate θ of the unknown parameter θbased on the observation vector r = [r1, r2, ...., rN ]
T
Note : r could eventually represent a continuos-time function r(t)through a suitable orthonormal base
estimator: θ = θ(r)
The problem is to find the estimator θ(r)which maximizes some performance metric(or minimizes a desired cost function)
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Data model
Data model (observation model)
θ deterministic unknown: p(r; θ) probability density function (PDF)
Classical estimation approach
Bayesian estimation approach
These PDFs are the laws that govern the effect of θ on the observation r
θ random, unknown: p(r, θ) = p(r|θ)p(θ), p(θ) prior information
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Square Error (SE) cost function
“Hit-or-Miss” cost function
C(²) = ²2
where ² = θ(r)− θ is the estimation error
Design Criteria for Bayesian Estimators (1/3)
Selection of a Cost Function
Recall
θ random, unknown: p(r, θ) = p(r|θ)p(θ), p(θ) prior information
C(²) =
½0 |²| ≤ ∆/21 |²| > ∆/2
Our goal is to find an estimate that minimizes
the expected value of the cost
Estimator design
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Minimum Mean Square Error (MMSE) Estimator
Design Criteria for Bayesian Estimators (2/3)
Minimizes the MSE
The estimation error has zero mean
Difficult to implement in the non-Gaussian case
where the expectation is with respect to the posteriori PDF
p(θ|r) =p(r|θ)p(θ)Rp(r|θ)p(θ) dθ
=p(r|θ)p(θ)
p(r)
MSE = Eθ,r©²2ª
θ(r) = Eθ|r{θ|r} =Rθ p(θ|r) dθ posteriori mean
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Maximum A Posteriori (MAP) Estimator
Design Criteria for Bayesian Estimators (3/3)
Minimizes the “hit-or-miss” cost function
No general formula is available
If r and θ are jointly Gaussian, it is equivalent to the MMSE estimator
θ(r) = argmaxθp(θ|r)
θ θ
p(θ|r)
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Minimum Mean Square Error (MMSE)
Design Criteria for Classical Estimators (1/6)
MSE = Ern(θ(r)− θ)2
o= Var (²) + Er{²}
Similar to the Bayesian case (here the expectation is only over r), but in general not realizable since the estimator depends on theparameter to be estimated!
We need other measures of quality of estimation process
Recall:
θ deterministic unknown: p(r; θ) PDF
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Design Criteria for Classical Estimators (2/6)
1) Impose zero bias (unbiased estimator)
Minimum Variance (MVU) Estimator
Er{²} = 0
2) Minimize the variance of estimation error
The MVU estimator does not always existWhen exists, no straightforward procedure are available to find the estimator
θ(r) = argminθ(r)
En(θ(r)− θ)2
o= argmin
θ(r)Var (²)
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The Cramer-Rao Lower Bound (CRB)
The CRB provides a minimum bound on the variance of any unbiased estimator:
CRB =³−E
n∂2 log p(r;θ)
∂θ2
o´−1
Def. Efficient estimator: if it attains the CRB for all θ
Design Criteria for Classical Estimators (3/6)
Var (²) = En(θ(r)− θ)2
o≥ CRB
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Maximum Likelihood (ML) Estimator
Not optimal in general, but straightforward
It is asymptotically efficient, i.e., asymptotically it is the MVU estimatorand the MSE tends to the CRB
If an efficient estimator exists, the MLE will produce it
Design Criteria for Classical Estimators (4/6)
θ(r) = argmaxθp(r; θ)
θθ
p(r; θ)
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Estimation of DC level in AWGN noise
This is a MVU estimator, the best we can do!
Design Criteria for Classical Estimators: example
rk = θ + nk for k = 1, 2, . . . , N
p(r; θ) = 1√2πσ2
expn−PN
k=1(rk−θ)22σ2
oE©n2kª= σ2
θ(r) = 1N
PNk=1 rk
Var (²) = Var³θ(r)
´= σ2
N
CRB = σ2
N
PDF:
ML estimator:
Performance:
CRLB:
E {²} = 0
Model:
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Least Squares (LS) Estimator
Not optimal in general, but straightforward
Design Criteria for Classical Estimators (6/6)
r = s(θ) + n
If n is Gaussian, then the LS estimator is equivalent to the ML estimator
Minimizing the LS error does not in general translate into minimizing the estimation error
No probabilistic assumptions are made about the observation
θ(r) = argminθ(r− s(θ))T ((r− s(θ))
Observation model
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Distance estimation:
Ranging
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Distance Estimation: Ranging
• Received Signal Strength (RSS)– direct RSS-distance mapping
– interferometric
• Time-Based– e.m. waves
– ultrasound
• Near field (phase difference between the E & H fields in the near range) (Q trace corporation)
Ranging between two nodes is the technique employed by two nodes in the network to determine the physical distance between them.
Node A Node B
Possible technologies
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Ranging based on RSS measurements
• Theoretical and empirical models are used to translate the difference between the transmitted signal strength and the RSS into a range estimate.
• Propagation effects (refraction, reflection, shadowing, and multipath) cause the attenuation to poorly correlate with distance
Inaccurate distance estimates
Time synch between nodes is not required
PR(dBm)
PR(dBm)
Unrealisticdeterministicchannel
Randomchannel
d d
-100-90-80-70-60-50-40-30-20-10
00,00 5,00 10,00 15,00 20,00 25,00 30,00
Distance (m)
RSS
I (db
m)
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RSS ranging: theoretical bound
•The MSE bound does not depend on signal structure•RSS ranging does not require time synchronization between nodes•No dedicated HW
Received power v.s. distance model (in dBm):
Cramer-Rao bound (CRB) for the distance estimation MSE
Note: the CRB provides a minimum bound on the variance of any unbiased estimator.
Var³d´≥
µln 10
10
σSγd
¶2.
Pr(d) = P0 − 10 γ log10 d+ S
P0 received power (in dBm) at a reference distance of 1 meterγ: path-loss exponent (typiacl values between 2 and 5)S: Gaussian random variable with zero mean and standard deviation σS (shad-owing spread). It accounts for large-scale random fluctuations (shadowing).
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Interferometric Ranging (1/2)
M. Maroti, P. Völgyesi, S. Dora, B. Kusy, A. Nadas, A. Ledeczi, G. Balogh, and K. Molnar, “Radio interferometricgeolocation,” in Proc. ACM Conference on Embedded Networked Sensor Systems, San Diego, USA, Nov. 2005, pp. 1–12.
•Transmitters A and B transmit sinusoids at slightly different frequencies
•The envelope of the received composite signal, after band-pass filtering, will vary slowly over time.
•The phase offset of this envelope can be measured using cheap low-precision RF chips, and it contains information about the difference in distance of the two links.
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Interferometric Ranging (2/2)
•To solve the unknown initial phase of the two transmitted sinusoids (no time synch is present), a similar measurement is made by node D in a different location
•The difference in phase offset measured at the two receivers only depends on the four distances. Hence, given a number of phase-offset-difference measurements, the unknown locations of the two receivers may be inferred.
Comments:
•Sub-meter precision can be achieved in outdoor environments with simple hardware•Problems with severe multi-path (e.g., indoor)
•Infrastructure required.
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Time-Based Ranging
• One-way Time-of Arrival (TOA) ranging
Node BNode A t1 t2
Node A Node B
• Two-way TOA ranging
τf =(t2−t1)+(t4−t3)
2
τf = t2 − t1
The distance information between a pair of nodes A and B (ranging) can beobtained using the measurement of the propagation delay or Time-of-Flight(TOF) τf = d/c, where d is the actual distance between A and B.
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Time-Difference-of-Arrival (TDOA) (1/3)
TDOA scheme A TDOA scheme B
Networks with infrastructure (anchor nodes) and accurate anchorssynchronization (e.g., through cable connections) are required
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Time-Difference-of-Arrival (TDOA) (2/3)
A typical approach uses a geometric interpretation to calculate the intersection of two or more hyperbolas: each sensor pair gives a hyperbola which represents the set of points at a constant range difference (time-difference) from two sensors
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Time-Difference-of-Arrival (TDOA) (3/3)
Networks with infrastructure (anchor nodes) and accurate anchorssynchronization (e.g., through cable connections) are required
TDOA scheme B suitable for extremely low complexity targets nodes (e.g., TAGs in UWB-RFID systems)
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All time-based ranging techniques require
time-of-arrival (TOA) estimation of the received signal
Hence, accurate TOA estimationand time measurement are required!
For example, τf = 1 ns means a distance d ≈ 30 cm
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Only an estimation of the real time t can be obtained due to node local oscillator frequency drift
Time Measurements (1/2)
Reasonable model:
T(t) = (1 + δ)t+ µ
δ: clock driftrelative to the correct rateµ: clock offset.
The rate of a perfect clock, dT(t)/dt, would equal 1 (i.e., δ = 0).
The clock performance is often expressed in terms of part-per-million (ppm)defined as the maximum number of extra (or missed) clock counts over a totalof 106 counts, i.e., δ · 106.
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Time Measurements (2/2)
Suppose that a node intends to generate a time delay of τd seconds,the effective generated delay τd in the presence of a clock drift δ would be
τd =τd1 + δ
In case a node has to measure a time interval of true durationτ = t2 − t1 seconds, the corresponding estimated value τ would be
τ = T(t2)− T(t1) = τ (1 + δ)
In both cases there is no dependence on the clock offset µ.
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One-Way TOA Ranging: effect of clock drifts
According to nodeA’s local time, the packet is transmitted at time t(A)1 = TA(t1)
(included as a timestamp in the packet) and it is received at node B’s local time
t(B)2 = TB(t2). Node B calculates the estimated propagation delay as
τf = t(B)2 − t
(A)1 = τf · (1 + δA) + t2 · (δB − δA) + µB − µA
clock offsets may be in the order of us, one-way raging tied to network synchronization
One-way ranging requires stringent network synchronization constraints whichare often not feasible in low-cost systems.
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Possible application of one-way TOA ranging
Ultrasound devices•propagation speed of acoustic waves (@340 m/s) is much lower than light-speed •synchronization errors can be several orders of magnitude smaller than the typical propagation delay values
•high positioning accuracy (3 cm)
Ultrasound technology has several disadvantages
•Hybrid technology•Propagation limited by walls (coverage)•Effect of temperature and pressure (calibration)•Power-hungry
A. Harter, A. Hopper, P. Steggles, A. Ward and P. Webster ”The anatomy of a Context-Aware Application”, In Wireless
Networks, Vol. 8, pp. 187-197, Feb. 2002.
Active Bat localization system
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Two-Way TOA Ranging: effect of clock drifts (1/2)
The effective response delay introduced by node B is τd/(1 + δB), whereas theestimated RTT denoted by τRT, according to node A’s time scale, is
τRT = 2 τf(1 + δA) +τd(1 + δA)
(1 + δB)
In absence of other information, node A derives the estimation of the propaga-tion time, τf, by equating the previous equation with the supposed round-triptime 2 τf + τd leading to an error
τf − τf = τf δA +ε τd
2(1 + δA − ε)≈
ε τd2(1 + δA − ε)
where ε , δA − δB .
τf: in the order of nanosecondsτd: in the order of microseconds/milliseconds (includes the time for packetacquisition, sync., channel estimation, etc.)
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Two-Way TOA Ranging: effect of clock drifts (2/2)
10−7
10−6
10−5
10−12
10−11
10−10
10−9
10−8
10−7
ε
Ran
ging
err
or (
s)
τd=1 µs
τd=10 µs
τd=100 µs
τd=1 ms
τd=10 ms
Low response delay must be ensured for high ranging accuracy
The ranging protocol must be implemented at PHY/MAC level
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Mitigation of clock frequency offsets
τf − τf = τf δA
Example:
• each node locally measures the (known) received packet duration and exchange its measure with the other node
• from the 2 measurements node A can evaluate a correction factorwhich applies to its estimated propagation time.
• In this case the error becomes
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Remarks
•Two-way ranging requires less stringent synchronization constraints compared to one-way ranging (relative clock drifts still affect ranging accuracy)
•Suitable for networks without infrastructure (e.g., ad hoc networks)
•Main scheme adopted in the IEEE 802.15.4a standard
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Time-of-Arrival (TOA) estimation
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Time-of-Arrival Estimation (TOA)
Consider the transmission of a single pulse p(t) in AWGN channel in the absence of other sources of error. The received signal is
Problem statement in an ideal scenario
Classical non-linear parameter estimation problem
r(t) =pEp p(t− τ) + n(t)
The problem is to obtain the best estimate for the TOA, τ , based on the receivedsignal r(t) observed over the interval [0, Tob).
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Maximum likelihood (ML) TOA estimator
The ML TOA estimator is asymptotically efficient in AWGN
matchedfilter
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The estimation Mean Square Error (MSE) of any unbiased estimation τ of τcan be bounded by the Cramer-Rao bound (CRB)
Var (τ ) = E©(τ − τ )2
ª≥ CRB
Theoretical limits in AWGN
The CRB is given by
CRB =N0/2
(2π)2Ep β2=
1
8π2 β2 SNR
where− SNR , Ep/N0− β2 represents the second moment of the spectrum P (f) of p(t) defined by
β2 ,R∞−∞ f
2|P (f)|2 dfR∞−∞ |P (f)|
2 df
− β: effective bandwidth
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Bound on ranging MSE
Increase the SNR towards very narrowband systems
To mitigate the effect of multipath in indoor environments, low frequency bands must be used not practical for small size devices due to large antennas
Increase the bandwidth towards ultra wideband (UWB) systems
Higher frequencies bands can be used. Multipath can be resolvable.Technique chosen by the IEEE 802.15.4a standard
Lower bound on ranging MSE
How to improve the accuracy?
Var³d´≥
c2
8π2 β2 SNR.
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Ultra-wide Bandwidth (UWB) Signals
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Definition of a UWB signal
UWB signal is formally defined as any signal that (FCC):
• occupies more than 500 MHz of spectrum or
• has a fractional bandwidth in excess of 20%
H L
H L
f fBF 0.2(f f ) / 2
−= ≥
+
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Brief history of UWB
• The Radio was born an UWB: spark gap transmission designs of Marconi in the late 1890s
• After the Marconi’s “4 seven” patent (1900) the radio became “tuned”
• Time-domain electromagnetics theory in early 1960s
• Short-pulse generators using tunnel diodes in early 1970s
• Applications to radar in 1970-80s
• The term “UWB” originated with the Defense Advanced Research Projects (DARPA) in a radar study undertaken in 1990
• First studies of possible application of UWB to communication systems during 1990s(Win-Scholtz)
• 2/2002 - FCC adopted the First Report and Order (RO) that permits the marketing and operation of certain types of UWB products
• 2/2007 – Implementation of an UWB radio regulatory framework for the EU
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Where do we find such a huge available bandwidth?
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Better to utilize already allocated bands using extremely
low power spectral densities (PSD)
FCC limits ensure that UWB emission levels are exceedingly small• at or below spurious emission limits for all radios• at or below unintentional emitter limits• lowest limits ever applied by FCC to any system
Part 15 limits equate to –41.25 dBm/MHz
For comparison, PSD limits for 2.4 GHz ISM and 5 GHz U-NII bands are +40 dB higher per MHz
Total emissions over several gigahertz of bandwidth are a small fraction of a milliwatt
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FCC masks
Different radiation masks were given for several UWB applications
Yes1.99 to 10.6 GHz Through-wall imaging and surveillance systems
No24 to 29 GHzVehicular radar systems
No3.1 to 10.6 GHzCommunications (indoor & outdoor) and medical systems*
Yes3.1 to 10.6 GHz Ground penetrating radars, wall imaging systems
User RestrictionsFrequency Band for Operation at Part 15 Limits
Application
*Indoor and outdoor communications devices have different out-of-band emission limits
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0.96 1.61
1.99
3.1 10.6
GPS Band
Example of indoor UWB mask
dBm/MHz
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0.96 1.61
1.99
3.1 10.6
GPS Band
Example of outdoor UWB mask (hand-held)
dBm/MHz
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EU mask
W. Hirt “The European UWB Radio Regulatory and Standards Framework: Overview and Implications”, ICUWB 2007. IEEE International Conference on Ultra-Wideband, 2007. Sept. 2007, Singapore.
A maximum mean EIRP density of -41.3 dBm/MHz is allowed in the 3.4 - 4.8 GHz bands provided that a low duty cycle restriction is applied in which the sum of all transmitted signals is less than 5% of the time each second and less than 0.5% of the time each hour, and provided that each transmitted signal does not exceed 5 milliseconds. Limits can be overcome using appropriate mitigation techniques. “10 second” rule introduced.
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0 1 2 3 4 5 6 7 8 9 10 11Frequency [GHz]
−90
−80
−70
−60
−50
−40
EIR
P d
ensi
ty [d
Bm
/MH
z]
FCC maskEU mask
Comparison between FCC and EU masks
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Worldwide regulations
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Why do we need UWB systems?
Motivations Main issues
•High temporal resolution (localization, multipath)
•High material penetration (according to the band used)
•Underlay technology (efficient spectrum usage)
•Multiple access
•Low probability of detection (LPD)
•Keep complexity low
•Large bandwidth antennas
•Interference management (coexistence)
Narrowband (30kHz)
Wideband CDMA (5 MHz)
UWB (several GHz)
Frequency
Part 15 Limit
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How to generate a UWB modulated signal
Conventional techniques over sinusoidal carrier
such as OFDM, DS-CDMA, FH-CDMA
Impulse radio
Base-band transmission of short pulses (monocycles)
Higher complexity
Lower complexity
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UWB Taxonomy
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Impulse Radio - UWB
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Impulse Radio UWB (IR-UWB)
Base-band generation of the signal using short duration pulses
•Low complexity (no RF and IF stages)
•Low consumption
•New antenna design criteria
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• Gaussian nth derivative monocycle
−0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5time (ns)
−4
−3
−2
−1
0
1
2
3
4
Am
plitu
de
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−50
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−30
−20
−10
0
10
norm
aliz
ed P
SD
[dB
]
normalized FCC maskpulse spectrum
The Gaussian monocycle
Example of Gaussian
6th derivative monocycle Spectrum
p0(t) = exp (−2π(t2/τ2p )) p(t) = p
(n)0 (t)
r(n−1)!
(2n−1)!πnτ (1−2n)p
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Example of simple impulse generator
From J.S. Lee, C. Nguyen e T. Scullion, IEEE Microwave Theory and Tech. 2001
Bit 0
Bit 0
Bipolar version realized at WiLAB – University of Bologna
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IR-UWB: signaling
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UWB signals are typically modulated pulse trains (low duty cycle)
At each bit several pulses (100-1000) are transmitted according to a certain “randomization” technique such as Time-hopping (TH) or Direct Sequence (DS) to allow multiple access (each user adopts a different “code”)
Modulation techniques include pulse-position modulation (PPM),pulse amplitude modulation (PAM) and others
Tb
δ Tf
IR-UWB: signaling
Randomized time hopping
PPM signaling
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Example: TH-PPM Impulse Radio
Pseudorandom Time Hopping coding (TH)
Tf : frame time
δ : modulation index
Ns: # pulses/symbol
Cj(k): pseudo-random
(PR) sequence(ex:1,3,6,2,…,6)
d: data bits
Tc: chip time
( )s
(k ) (k ) (k )f j c j/ N
js (t) p t jT c T d
∞
⎢ ⎥⎣ ⎦=−∞
= − − − δ∑Transmitted signalK-th user
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UWB propagation
and channel models
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The UWB channel
TxUWB
Rx UWB
Direct path
Multi-path
Multipath propagation
Narrowband channel:
Wideband channel
Ultra wideband channel
Fading large link budget margin
Echoes signal distortion
Single paths are typically not resolvable, i.e., echoes amplitudes exhibit fading
Echoes signal distortion
Single paths may be resolved potential diversity gain
g(t): channel gainh(t): channel impulse responses(t): transmitted signalr(t): received signal
r(t) = g(t) · s(t)
r(t) = h(t)⊗ s(t)
r(t) = h(t)⊗ s(t)
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UWB Propagation Experiment
• Modern building with officesand labs
• Multipath profilesT = 300ns
• 14 rooms 49 equidistant points
• 7 x 7 measurement gridwith 90 cm side
Moe Z. Win, and Robert A. Scholtz, “Characterization of Ultra-Wide Bandwidth Wireless Indoor Channels: A Communication-Theoretic View”, IEEE Journal On Selected Areas In Communications, Vol. 20, No. 9, December 2002
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Room F1
LOS high SNR
Room P
NLOS high SNR
Room H
NLOS medium SNR
Room B
NLOS low SNR
Example of channel impulse responses
In general, dense multipath is present composed of tens or hundreds paths
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Channel model for dense multipath: IEEE 802.15.4a
The IEEE 802.15.4a UWB channel model is based on an extended version of the classical Saleh-Valenzuela (SV) indoor channel model, where multipathcomponents arrive at the receiver in groups (clusters) following the Poisson distribution. According to the SV model, the complex baseband channel impulse response is given as
The Power Delay Profile (PDP) is assumed exponential negative within each cluster
h0(t) =PK
k=0
PLl=0 ak,le
jφk,lδ (t− Tk − τk,l) ,
ak,l: amplitude of the lth path in the kth clusterTk: delay of the kth clusterτk,l: delay of the lth path relative to the kth cluster arrival time Tkφk,l are uniformly distributed in the range [0, 2π)The number of clusters K is assumed to be Poisson distributed.The ray arrival times τk,l are modeled with mixtures of two Poisson processesThe small-scale fading, which characterizes the path amplitudes ak,l, follows aNakagami-m distribution
Λk,l = En|ak,l|
2o∝ exp(−τk,l/²k) ²k is the intra-cluster decay time constant.
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Simplified channel model for lower frequencies
Another widely adopted model is the dense multipath model with a single cluster composed of L independent equally spaced paths and exponential PDP. In this case the real impulse response is given by
Exponential negative PDP
h(t) =PL
l=0 al pl δ (t− τl)
τl = τ1 + (l − 1)∆∆: resolvable time intervalal: path amplitude with Nakagami-m statisticspl is a r.v. which takes, with equal probability, the values {−1,+1}
Λl = En|al|
2o= (e∆/²−1)e−∆(l−1)/²
e∆/²(1−eL∆/²) ²: decay time constant.
A. F. Molisch, D. Cassioli, C.-C. Chong, S. Emami, A. Fort, B. Kannan, J. Karedal, J. Kunisch, H. Schantz, K. Siwiak,and M. Z. Win, “A comprehensive standardized model for ultrawideband propagation channels,” IEEE Trans. AntennasPropag., vol. 54, no. 11, pp. 3151–3166, Nov. 2006, Special Issue on Wireless Communications.
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Ranging using UWB signals
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Impulse Radio UWB (IR-UWB)
Example of Gaussian
6th derivative monocycle
Base-band generation
of the signal
•Low complexity (no RF and IF stages)
•Low consumption
Spectrum
−0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5time (ns)
−4
−3
−2
−1
0
1
2
3
4
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plitu
de
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−50
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−20
−10
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10
norm
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SD
[dB
]
normalized FCC maskpulse spectrum
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Theoretical Performance Limits
Theoretical performance limits (bounds) serve as useful benchmarks in assessing the performance of newly developed TOA estimation techniques
The performance of the TOA estimator, like all non-linear estimators, is characterized by the presence of distinct SNR regions corresponding to different modes of operation:
- Low SNR region (a priori region), signal observations provide little new information- High SNR region (asymptotic region), the MSE is accurately described by the CRB- Medium SNR (transition region or ambiguity region), observations are subject to ambiguities that are not accounted for by the CRB
This behaviour is referred to as the threshold effect
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Improved bounds
Unfortunately, the CRB is not accurate at low and moderate SNR and/or when the observation time is short improved bounds
The Ziv-Zakai Lower Bound
The Ziv-Zakai bound (ZZB) can be applied to a wider range of SNRs, but more complex than CRB for analytical evaluation
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The Ziv-Zakai Lower Bound
where Pmin (τ ) is the error probability of the classical binary detection schemewith equally probable hypothesis:
H1 : r(t) ∼ p {r(t)|τ}
H2 : r(t) ∼ p {r(t)|τ + z}
When τ is uniformly distributed in [0, Ta), the ZZB is
ZZB =1
Ta
Z Ta
0
z (Ta − z)Pmin (z) dz
In AWGN the minimum attainable probability of error becomes
Pmin (z) = Q
µqSNR (1− ρp(z))
¶where Q (·) is the Gaussian Q-function and ρp(z) is the autocorrelation functionof p(t).
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0 5 10 15 20 25 30 35 40SNR (dB)
10-13
10-12
10-11
10-10
10-9
10-8
10-7
RM
SE
(s)
CRB, RRC, τp=3.2 ns
ZZB, RRC, τp=3.2 ns
CRB, RRC, τp=1 ns
ZZB, RRC, τp=1 ns
CRB, Gauss. deriv., n=2
ZZB, Gauss. deriv., n=2
CRB, Gauss. deriv., n=6
ZZB, Gauss. deriv., n=6
TOA estimation theoretical limits using UWB pulses
From D. Dardari, C.-C. Chong, and M. Z. Win, “Improved lower bounds on time-of-arrival estimation error in realistic UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2006, (Waltham, MA, USA), pp. 531–537, Sept. 2006.
CRB and Ziv-Zakai Bound in AWGN (single path)
30 cm
3 cm
3 mA priori RMSE Ta/
√12 A priori region
Asymptotic region
Ambiguity region
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Main sources of error
in TOA Ranging
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Main Issues in UWB TOA Estimation
Rich multipath(ambiguities in first path detection, paths overlapping)
Non Line-of-Sight (NLOS)
conditions•direct path blockage•extra propagation delay due to different e.m. propagation speeds
Interference(narrowband and wideband)
In addition:• clock drift (time synch algorithms)• design of low-complexity TOA estimators
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TOA Estimation in Multipath Environments
In general, dense multipath is composed of tens or hundreds paths.It may be difficult to recognize the first path, especially at low and medium SNRs
detection of first path may be challengingPaths could be partially overlapped (not resolvable channel)
Moe Z. Win, and Robert A. Scholtz, “Characterization of Ultra-Wide Bandwidth Wireless Indoor Channels: A Communication-Theoretic View”, IEEE Journal On Selected Areas In Communications, Vol. 20, No. 9, December 2002
r(t) =pEp
LXl=1
αl p(t− τl) + n(t)p(t)
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TOA Estimation in Multipath Environments
Received signal
r(t) =pEp
LXl=1
αl p(t− τl) + n(t)
Problem formulation
• we are interested in the estimation of the ToA, τ = τ1, of the direct path byobserving the received signal r(t) within the observation interval [0, Tob);
• we consider τ to be uniformly distributed in the interval [0, Ta), withTa < Tob;
• set of nuisance parameters U = {τ2, τ3, . . . , τL,α1,α2, . . . ,αL}
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CRB and ZZB for TOA Estimation with Multipath
5 10 15 20 25SNR (dB)
10-12
10-11
10-10
10-9
10-8
RM
SE
(s)
CRB ZZB, CM1ZZB, CM4ZZB, CM5
Using the IEEE802.15.4a channel model
D. Dardari, A. Conti, U. Ferner, A. Giorgetti, and M. Z. Win, “Ranging with Ultrawide Bandwidth Signals in MultipathEnvironments”, Proc. of IEEE (Special Issue on UWB Technology & Emerging Applications), Feb. 2009.
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ZZB for TOA Estimation using Measured Data
0 5 10 15 20 25 30SNR (dB)
10-12
10-11
10-10
10-9
10-8
10-7
RM
SE
(s)
LOS (Rx2)LOS (Rx3)NLOS (Rx4)NLOS (Rx5)NLOS (Rx6)NLOS (Rx7)NLOS (Rx8)
D. Dardari, C.-C. Chong, and M. Z. Win, “Improved lower bounds on time-of-arrival estimation error in realistic UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2006, (Waltham, MA, USA), pp. 531–537, Sept. 2006.
C. –C. Chong and S. K. Yong, “A generic statistical based UWB channel model for high-rise apartments,” IEEE Trans. Antennas and Propagation, vol. 53, no. 8, pp. 2389-2399, Aug. 2005.
high-rise apartment
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ML TOA Estimator in Multipath Environments
Problems:• Implementation at Nyquist sampling rate or higher difficult with UWB
signals• High complexity
looking for sub-optimal schemes
The ML estimate of τ is τ = argmaxτ
{χH(τ )R−1(τ )χ(τ )}, where R(τ ) is the
autocorrelation matrix of p(t) and
χ(τ ) ,Z Tob
0
r(t)
⎡⎢⎢⎢⎢⎢⎢⎣p(t− τ1)p(t− τ2)
.
.
.p(t− τL)
⎤⎥⎥⎥⎥⎥⎥⎦ dt
When channel parameters are unknown, TOA estimation in multipath environ-ments is closely related to channel estimation, where path amplitudes and TOAτ = [τ1, τ2, . . . , τL]
T are jointly estimated using, for example, a ML approach
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Reducing the Estimator Complexity
Examples:
-Sub-optimal ML
-Generalized ML
-Simple thresholding
-Multi-resolution
-Delay & average (noisy template)
-Two-stages approaches
-Energy detection based
-……………………
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Sub-optimal ML Algorithms
• They can be derived from the ML channel estimation criterion and based on a simple peak detection process
– Single Search– Search and Subtract– Search, Subtract and Readjust
These algorithms essentially involve the detection of N largest positive and negative values of the MF output and the determination of the corresponding time locations .
While these algorithms are equivalent when the multipaths are separable, the last two algorithms
take into account the effects of a non-separable channel (overlapped echoes)
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Single Search
1k 2k
3k
• The delay and amplitude vectors are estimated with a single look.
( )r t ( )y t
MF
( )y t
Peak Detectorˆ ˆ,
i ik kcτ
1,2,...,ik N=
ABS ()
( )y t
3k
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Search and Subtract
( )r t
1ˆkτ
( ) ( ) ( )1ir t r t r t−= −
MF Peak Detect
1,2,...,ik N=
Path estimator
+
( ) ( )ˆ ˆ ˆi ii k kr t c p t τ= −
( )1ir t−
ABS ()
This algorithm provides a way to detect multipath components in a non-separable channel.
( )y t ( )y t
ˆ ˆ,i ik kcτ
path 2 path 1
received signal
received signalafter subtract
( )r t
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Search Subtract and Readjust
Peak Detect
{ }1
ˆ ˆj
i
k jc c
==
ˆikτ
Peak estimator
Path estimator
( ) ( )1ˆ ˆ ˆ
j j
ii k kj
r t c p t τ=
= −∑
( ) ( )ph t p T t= −
MF
++ ( )1ir t−
ABS ()
( ) ( ) ( )1ir t r t r t−= −
1,2,...,ik N=
( )y t ( )y t
( )r t
So far (Search and Subtract) the delay and amplitude of each path are estimated separately at each step; in this algorithm a joint estimation of the amplitudes of different paths is introduced.
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Simple Thresholding
The optimum choice of η depends on channel statistics and SNR:
• Small η → high probability of early detection prior to the first path (earlyTOA estimation) due to noise and interference
• Large η→ low probability of detecting the first path and a high probabilityof detecting an erroneous path (late TOA estimation) due to fading
Threshold η
threshold
Another classical approach is the Thresholding Estimator
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Algorithmstested
Lower complexity
Higher complexity
Simple thresholding, peak detection
Iterative cancellation techniques (to deal with paths overlapping)
Main observations:
-There is no a large performance difference between simple and complex algorithms considered (especially at medium and large SNRs)
-Ranging resolutions on the order of 10cm are achievable!
Decreasing SNR
Performance in Realistic EnvironmentsResults from: C. Falsi, D. Dardari, L. Mucchi, and M. Z. Win, “Time of arrival estimation for UWB localizers in realistic environments,” EURASIP J. Appl. Signal Processing (Special Issue on Wireless Location Technologies and Applications), 2006.
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Sub-Nyquist Sampling Rate TOA Estimators
TOA estimators based on energy detection (ED) can operate at sub-Nyquist sampling rate low complexity
The TOA resolution is bounded by the ED integration time
• T[.]: pre-processing filter– to improve the first path detection– to mitigate the effect of the interference
• First path detector: several algorithms can be adopted
Asymptotic MSE = T 2int/12
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Example of ranging preamble structure
Each user has a different time-hopping sequenceto allow multi-user communication
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ED-based TOA estimation algorithms
• Simple thresholding• P-MAX• Backward search• Jump Back and Search Forward• ………
Collected energy vector
Possible algorithms
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Simple thresholding
Decision vector
Detect the time slot within the observation interval that contains the first pathby comparing each element of {zk} to a fixed threshold η.The first threshold crossing event is taken as the estimate of the TOA
The optimum choice of η depends on channel statistics and SNR:
• Small η → high probability of early detection prior to the first path (earlyTOA estimation) due to noise and interference
• Large η→ low probability of detecting the first path and a high probabilityof detecting an erroneous path (late TOA estimation) due to fading
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P-Max
Decision vector
The P-Max criteria is based on the selection of the earliest sample among theP largest in {zk}.
TOA estimation performance depends on the parameter P , where P can beoptimized according to received signal characteristics
- D. Dardari, C.-C. Chong, and M. Z. Win, “Analysis of threshold-based ToA estimators in UWB channels,” in European Signal Processing Conference, EUSIPCO 2006, Florence, ITALY, Sep. 2006.
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Backward Search
Decision vector
The search begins from the largest sample in {zk}, with index kmax, and thesearch proceeds element by element backward in a window of length Wsb untilthe sample-under-test goes below the threshold η
- I. Guvenc and Z. Sahinoglu, “Threshold-based TOA estimation for impulse radio UWB systems,” in Proc. IEEE Int. Conf. on Utra-Wideband (ICU), Zurich, Switzerland, Sep 2005, pp. 420–425.
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Jump Back and Search Forward
Decision vector
The leading edge of the signal is searched element-by-element in a window oflength Wsb samples preceding the strongest one.The search proceeds forward until the sample-under-test crosses the threshold
The optimal selection ofWsb and η depend on the received signal characteristics
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10 15 20 25 30 35 40SNR (dB)
10-10
10-9
10-8
10-7
RM
SE
(s)
MAX
P-Max, P=3
JBSF, Wsb=20
Simple Thresholding
SBS, Wsb=20
SBSMC, Wsb=30, D=5
Performance comparison between TOA est. techniques
From: D. Dardari, A. Conti, U. Ferner, A. Giorgetti, and M. Z. Win, “Ranging with Ultrawide Bandwidth Signals in Multipath Environments”, Proc. of IEEE (Special Issue on UWB Technology & Emerging Applications), Feb, 2009.
Error floor
Signal bandwidth 1.6 GHzCenter frequency 4 GHzED integration time 2 nsPreamble length 400 pulsesFrame duration 120 nsIEEE802.15.4a (CM4)channel model
Tint/√12
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Interference effects
UWB systems are expected to work in coexistence with other UWBand narrowband systems as an underlying technology
Both wideband interference (WBI) and narrowband interference (NBI)can be present and degrade the ToA estimation
Either non-linear and linear 2D filtering techniques can be applied to mitigate the effect of the interference
- Z. Shainoglu and I. Guvenc, “Multiuser interference mitigation in noncoherent UWB ranging via nonlinear filtering,” EURASIP J. Wireless Communications and Networking, vol. 2006, pp. 1–10, 2006.
- D. Dardari, A. Giorgetti, and M. Z. Win, “Time-of-arrival estimation in the presence of narrow and wide bandwidth interference in UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2007, Singapore, Sep. 2007.
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Interference Mitigation Techniques
Min Filter
Min Filter+Differential Filter
Each column of {vn,k} can be processed as follows
zk =
Nt−HXn=0
min{vn,k, vn+1,k, . . . , vn+H−1,k},
where k = 0, . . . ,K − 1, and H is the length of the filter.
To improve the estimation performance in the presence of both NBI and WBI,the min filter is first applied to each matrix column to produce the intermediatevector {zk}.The vector {zk} is further processed (differential filter) as follows
zk = zk − zk+1, k = 0, . . . , K − 1 ,
with zK = 0.
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Energy matrix filtering examples
First step: after the min filterBefore filtering
Second step:
after the differential filter
True ToA
True ToA
True ToA
WBI+NBI
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10 15 20 25 30 35 40SNR (dB)
10-10
10-9
10-8
10-7
RM
SE (s
)
Averaging filterDifferential filterMin filterMin & differential filters
w/o interf.
NBI & MUI
Performance of the threshold-based estimator with different 2D filtering tech-niques in the presence of both NBI, INR = 35 dB, and WBI, SIR = −15 dB.
Nsym = 400: preamble length.
From D. Dardari, A. Giorgetti, and M. Z. Win, “Time-of-arrival estimation in the presence of narrow and wide bandwidth interference in UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2007, Singapore, Sep. 2007.
Performance comparison between mitigation techniques
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NLOS condition: extra propagation delay (1/2)
D. Dardari, A. Conti, J. Lien, and M. Z. Win, “The effect of cooperation in UWB based positioning systems usingexperimental data,” EURASIP Journal on Advances in Signal Processing, Special Issue on Cooperative Localization inWireless Ad Hoc and Sensor Networks, 2008.
The extra delay ∆τ introduced by a homogeneous material with thickness dWis given by
∆τ = (√²r − 1)
dWc
where ²r is the relative electrical permittivity of the material.
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Extra propagation delay effect leads to biased estimations, typically some nanoseconds
Possible solutions:
• larger number of anchor nodes
• knowledge of some a priori information (e.g., scenario layout)
• cooperation among nodes
Ranging error on the order of 50 cm!
NLOS condition: extra propagation delay (2/2)
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The IEEE 802.15.4a standard
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Overview of IEEE 802.15.4 (ZigBee)
Features:• Low Data Rate• Low Power Consumption• Low Cost• Self-Organization and Flexible Topology
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The IEEE 802.15.4a
As an amendment to 802.15.4 for an alternative PHY to provide (2007):
• High precision ranging/location capability• Ultra low power• Lower cost• High aggregate throughput• Scalability to data rates• Longer range
Asset tracking
Home automation
and.. WSNs, health care monitoring…
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Physical layers
The amendment adds two new PHYs:
• 150 - 650 MHz and 3.1 - 10.6 GHz Ultra-Wide Band (UWB)– Impulse Radio (IR) based signaling– Mandatory nominal data rate of 1 Mbps– Modulation: Combination of BPM (burst position mod.) and BPSK
• 2450 MHz Chirp Spread Spectrum (CSS)
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UWB PHY: Channels
A compliant UWB device shall be capable of transmitting in at least one of three specified bands.
• Sub-GHz Band: 150 – 650 MHz
• Low Frequency Band (LFB): 3244 - 4742 MHz
• High Frequency Band (HFB): 5944 – 10234 MHz
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UWB-PHY: Bursts
• Each information-bearing symbol is represented by a burst of short time duration pulses
• The duration of an individual pulse is nominally considered to be the length of a chip
• Chip duration is equal to 2.02429 ns (Bandwidth > 494MHz)
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UWB PHY: Modulation
Combination of burst position modulation (BPM) and binary phase shift keying (BPSK)
A UWB PHY symbol carries two bits of information: one bit for the position of a burst and the other bit for the phase (parity) of the burst.
• Each symbol can have 8, 16, 32, 64, and 128 possible slots for aburst.
• A symbol period is divided into two BPM intervals, and a burst occurs one of two intervals.
• A burst is formed by grouping 2n, n=0, 1, 2, ..., 9 consecutive chips.
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The symbol structure
The UWB-PHY supports both coherent and non-coherent receivers.
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UWB-PHY: Spreading
• The UWB PHY provides interference suppression through scrambling and time-hopping.
• Scrambling: each symbol contains a single burst of pulses, which are scrambled by a time-varying scrambling sequence.
• Time Hopping: each burst position varies from one symbol to the next according to a time hopping code.
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Device Types
• Fully-function device (FFD)– Any topology– PAN coordinator– Talks to any other device– Capable of perform energy detection (ED) and active
scans
• Reduced-function device (RFD)– Limited to star topology– Talks only to an FFD– Simple implementation and application
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Topology
• Star topology: Communication is established between devices and a single central controller,called the PAN coordinator.
• Peer-to-Peer Topology: Any device can communicate with any other device as long as they are in range of one another.
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Ranging in the IEEE802.15.4a standard (1/2)
• Ranging is optional (UWB PHY only)
• Main ranging protocol: Two-way TOA ranging (others possible)
• Private ranging protocol to protect against malicious devices (optional)
• Both coherent and non coherent estimation
Preamble
[16,64,1024,4096] symbols
Start of frame delimiter, SFD
[8,64] symbols
PHY header Data field
IEEE 802.15.4a packet structure
Preamble: acquisition, channel sounding and leading edge detectionSFD: frame synchronization, ranging counter management.
Designed to minimize the frame synchronization error
•The preamble and SFD lengths are specified by the application based on channel conditionsand receiver capabilities (coherent/non-coherent)
•The application bases its choice on figure of merit reports from PHY on good range measurement is
•Longer lengths are preferred for non-coherent receivers to improve SNR via processing gain
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Ranging in the IEEE802.15.4a standard (2/2)
• Each symbol is composed of a ternary sequence taken from a set of 8 sequences
• Each ternary sequence, and its non coherent version, has ideal periodic autocorrelation function (no side lobes) so that what is observed at the receiver is only the channel response
Example of one the 8 possible length-31 ternary sequence
[-000+0-0+++0+-000+-000+-+++00-+0-00]
Autocorrelation function
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Some advanced issues
• Secure ranging• Cognitive ranging• Robust ranging (interference mitigation) • Passive ranging and localization• New generation RFID systems• ……….
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Secure Ranging
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Secure Ranging
Ranging can be subjected to hostile attacks in certain environments.
In order to make impostor and snooper attacks more difficult, the IEEE 802.15.4a standard includes an optional private ranging mode
Node A Node B
Private ranging
After a preliminary authentication step, nodes exchange information, via a secure communication protocol, on both the sequences to be used in the next ranging cycle as well as their timestamp reports
The response delay τd is kept secret
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Interference mitigation &
Cognitive Ranging
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Spectral Skyline
• Total Congestion• No room to build new high-bandwidth services
Source: Turi Aytur, WiMedia Technical Overview, Realtek Semiconductor
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UWB: the quiet neighbor
• Wide bandwidth and very low power spectral density• “Underlay” technology coexists with other services
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Narrow Band Interference (NBI)
• UWB system operate over extremely wide frequency bands, where various narrow band technology operate with much higher power levels.
• The narrowband system are affected from the UWB signal only at anegligible level.
• The influence of NBI on the UWB system may jam the UWB receiver.
Coexistence
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UWB towards Cognitive Radio paradigm
In this line, CR based on UWB could represent a more complete solution as it:
begins to transmit inside those bands (Agile spectrum generation)
frequency
Pow
er S
pect
rum
eventually being get out if needed when the primary users show up
frequency
Pow
er S
pect
rum
actively looks for unused spectrum (Spectrum sensing)
frequency
Pow
er S
pect
rum
holes
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Cognitive Ranging
Target:
Find the optimal transmitted signal spectrum shape which maximizes the theoretical ranging accuracy
frequency
Pow
er S
pect
rum
We expect that a higher ranging accuracy can be achieved if we tolerate the presence of interference under certain power mask constraints (underlay transmission) .
frequency
Pow
er S
pect
rum
Transmitting only in the spectrum holes could not be the best solution
D. Dardari, Y. Karisan, S. Gezici, A. A. D’Amico, and U. Mengali, “Performance limits on ranging with cognitive radio,”in International Workshop on Synergies in Communicaions and Localization (SyCoLo 2009), Dresden, Germany, June 2009.
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Passive Ranging and Localization
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Passive localization
• Besides the localization of “friendly” collaborative objects (active tags), passive geolocation, i.e., the possibility of detecting and tracking non collaborative objects (targets) is gaining an increasing attention.
• Attractive to monitoring critical environments:– Power plants– Reservoirs– Critical structures vulnerable to attacks
• Attractive for rescue in disaster scenarios (e.g., to quickly localize people trapped in collapsed buildings or in the presence of dense smoke…)
targets typically human beings or vehicles
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Anti-intruder radar systems
• To this aim, a wireless infrastructure composed of cooperative UWB nodes could represent a cheap solution thanks to high resolutioncapabilities and low cost signal processing techniques
• Tactical wireless sensor networks• Radar sensor networks• Multistatic UWB radars
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• Monostatic radarTX and RX are colocated
• Bistatic radarTx and RX are separated by a distance comparable to the target
distance
Radar basics
target
target
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• Multistatic radarTxs and RXs are separated by a distance comparable to the target
distance
– Multiple TXs and one RX– One TX and multiple RXs
• They do not suffer from coupling between TX and RX• Can detect stealth targets• RXs are not detectable• They do not require directional antennas to locate the target• With more RX we can increase the radar sensitivity and counteract
fading, shadowing and clutter• They require proper information exchange (cooperation is needed)
Multistatic radar basics
target
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Selected references
D. Dardari, A. Conti, U. Ferner, A. Giorgetti, and M. Z. Win, “Ranging with Ultrawide Bandwidth Signals in MultipathEnvironments”, Proc. of IEEE (Special Issue on UWB Technology & Emerging Applications), Feb. 2009.
I. Guvenc and Z. Sahinoglu, “Threshold-based TOA estimation for impulse radio UWB systems,” in Proc. IEEE Int. Conf. on Utra-Wideband (ICU), Zurich, Switzerland, Sep 2005, pp. 420–425.
S. Gezici, Z. Tian, G. B. Giannakis, H. Kobayashi, A. F. Molisch, H. V. Poor, and Z. Sahinoglu, “Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks,” IEEE Signal Processing Mag., vol. 22, pp. 70–84, Jul. 2005.
D. Dardari, C.-C. Chong, and M. Z. Win, “Improved lower bounds on time-of-arrival estimation error in realistic UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2006, (Waltham, MA, USA), pp. 531–537, Sept. 2006.
R. Verdone, D. Dardari, G. Mazzini, A. Conti, Wireless Sensors and Actuator Networks: Technologies, Analysis and Design, Elsevier, 2008
Z. Sahinoglu, S. Gezici, I. Guvenc, “Ultra-wideband positioning systems: theoretical limits, ranging algorithms, and protocols”, Cambridge University press, 2008.
D. Dardari, C.-C. Chong, and M. Z. Win, “Threshold-based time-of-arrival estimators in UWB dense multipath channels,”IEEE Trans. Commun., vol. 56, no. 8, Aug. 2008.
D. Dardari, A. Giorgetti, and M. Z. Win, “Time-of-arrival estimation in the presence of narrow and wide bandwidth interference in UWB channels,” in IEEE International Conference on Ultra-Wideband, ICUWB 2007, Singapore, Sep. 2007.
Z. Shainoglu and I. Guvenc, “Multiuser interference mitigation in noncoherent UWB ranging via nonlinear filtering,”EURASIP J. Wireless Communications and Networking, vol. 2006, pp. 1–10, 2006.
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Selected references
D. Dardari, A. Conti, J. Lien, and M. Z. Win, “The effect of cooperation in UWB based positioning systems usingexperimental data,” EURASIP Journal on Advances in Signal Processing, Special Issue on Cooperative Localization inWireless Ad Hoc and Sensor Networks, 2008.
C. Falsi, D. Dardari, L. Mucchi, and M. Z. Win, “Time of arrival estimation for UWB localizers in realistic environments,”EURASIP J. Appl. Signal Processing (Special Issue on Wireless Location Technologies and Applications), 2006.
P. Cheong, A. Rabbachin, J. Montillet, K. Yu, and I. Oppermann, “Synchronization, TOA and position estimation forlow-complexity LDR UWB devices,” in Proc. of IEEE Int. Conf. on Ultra-Wideband (ICUWB), Zurich, SWITZERLAND,Sep 2005, pp. 480–484.
J.-Y. Lee and R. A. Scholtz, “Ranging in a dense multipath environment using an UWB radio link,” IEEE J. Sel. AreasCommun., vol. 20, no. 9, pp. 1677–1683, Dec. 2002.
Y. Shen and M. Z. Win,“Effect of path-overlap on localization accuracy in dense multipath environments,” in Proc. IEEE Int. Conf. on Commun., Beijing, CHINA, May 2008.
Y. Shen and M. Z. Win, “Fundamental limits of wideband localization accuracy via Fisher information,” in Proc. IEEEWireless Commun. and Networking Conf., Kowloon, HONG KONG, Mar. 2007, pp. 3046–3051.
B. Alavi and K. Pahlavan, “Modeling of the TOA-based distance measurement error using UWB indoor radiomeasurements,” IEEE Commun. Lett., vol. 10, no. 4, pp. 275–277, Apr. 2006.
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Selected references
C.-C. Chong and S. K. Yong, “A generic statistical-based UWB channel model for high-rise apartments,” IEEE Trans.Antennas Propag., vol. 53, pp. 2389–2399, 2005.
D. Cassioli, M. Z. Win, and A. F. Molisch, “The ultra -wide bandwidth indoor channel: from statistical model tosimulations,” IEEE J. Sel. Areas Commun., vol. 20, no. 6, pp. 1247–1257, Aug. 2002.
A. F. Molisch, D. Cassioli, C.-C. Chong, S. Emami, A. Fort, B. Kannan, J. Karedal, J. Kunisch, H. Schantz, K. Siwiak,and M. Z. Win, “A comprehensive standardized model for ultrawideband propagation channels,” IEEE Trans. AntennasPropag., vol. 54, no. 11, pp. 3151–3166, Nov. 2006, Special Issue on Wireless Communications.
M. Z. Win and R. A. Scholtz, “Impulse radio: How it works,” IEEE Commun. Lett., vol. 2, no. 2, pp. 36–38, Feb. 1998.
“IEEE standard for information technology - telecommunications and information exchange between systems - local andmetropolitan area networks - specific requirement part 15.4: Wireless medium access control (MAC) and physical layer(PHY) specifications for low-rate wireless personal area networks (WPANs),” IEEE Std 802.15.4a-2007 (Amendment toIEEE Std 802.15.4-2006), pp. 1–203, 2007.
Moe Z. Win, and Robert A. Scholtz, “Characterization of Ultra-Wide Bandwidth Wireless Indoor Channels: A Communication-Theoretic View”, IEEE Journal On Selected Areas In Communications, Vol. 20, No. 9, December 2002
S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2,pp. 201–220, Feb. 2005.
M. Chiani and A. Giorgetti, “Coexistence between UWB and narrowband wireless communication systems,” Proc. ofIEEE, Special Issue on UWB Technology & Emerging Applications, Feb. 2009.
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Selected references
M. Maroti, P. Völgyesi, S. Dora, B. Kusy, A. Nadas, A. Ledeczi, G. Balogh, and K. Molnar, “Radio interferometricgeolocation,” in Proc. ACM Conference on Embedded Networked Sensor Systems, San Diego, USA, Nov. 2005, pp. 1–12.
B. Zhen, H.-B. Li, and R. Kohno, “Clock management in ultra-wideband ranging,” Proc. IST Mobile and WirelessCommun. Summit, pp. 1–5, Jul. 2007.
F. Sivrikaya and B. Yener, “Time synchronization in sensor networks: a survey,” IEEE Netw., vol. 18, no. 4, pp. 45–50,2004.
Y. Jiang and V. Leung, “An asymmetric double sided two-way ranging for crystal offset,” Int. Symp. on Signals, Systemsand Electronics (ISSSE), pp. 525–528, July 30 2007-Aug. 2 2007.
I. Guvenc, C.-C. Chong, and F. Watanabe, “NLOS identification and mitigation for UWB localization systems,” in Proc.IEEE Wireless Commun. and Networking Conf., Kowloon, HONG KONG, Mar. 2007, pp. 1571–1576.
C.-C. Chong, F. Watanabe, and M. Z. Win, “Effect of bandwidth on UWB ranging error,” in Proc. IEEE Wireless Commun. and Networking Conf., Kowloon, HONG KONG, Mar. 2007, pp. 1559–1564.
W. Suwansantisuk and M. Z. Win, “Multipath aided rapid acquisition: Optimal search strategies,” IEEE Trans. Inf. Theory, vol. 53, no. 1, pp. 174–193, Jan. 2007.
H. L. Van Trees, Detection, Estimation, and Modulation Theory, 1st ed. New York, NY 10158-0012: John Wiley &Sons, Inc., 1968.
D. Chazan, M. Zakai, and J. Ziv, “Improved lower bounds on signal parameter estimation,” IEEE Trans. Inf. Theory,vol. 21, no. 1, pp. 90–93, Jan. 1975.
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Selected references
J. Zhang, R. A. Kennedy, and T. D. Abhayapala, “Cramer-rao lower bounds for the synchronization of UWB signals,” in EURASIP Journal on Wireless Communications and Networking, vol. 3, 2005.
V. Lottici, A. D’Andrea, and U. Mengali, “Channel estimation for ultra-wideband communications,” IEEE J. Sel. AreasCommun., vol. 20, no. 9, pp. 1638–1645, Dec. 2002.
H. Zhan, J. Ayadi, J. Farserotu, and J.-Y. Le Boudec, “High-resolution impulse radio ultra wideband ranging,” Proc. ofIEEE Int. Conf. on Ultra-Wideband (ICUWB), pp. 568–573, Sep. 2007.
S. H. Song and Q. T. Zhang, “Multi-dimensional detector for UWB ranging systems in dense multipath environments,”IEEE Trans. Wireless Commun., vol. 7, no. 1, pp. 175–183, 2008.
A. Rabbachin, I. Oppermann, and B. Denis, “ML time-of-arrival estimation based on low complexity UWB energydetection,” in Proc. of IEEE Int. Conf. on Ultra-Wideband (ICUWB), Waltham, MA, Sep. 2006, pp. 599–604.
A. A. D’Amico, U. Mengali, and L. Taponecco, “Energy-based TOA estimation,” IEEE Trans. Wireless Commun., vol. 7,no. 3, pp. 838–847, March 2008.
Z. Lei, F. Chin, and Y.-S. Kwok, “UWB ranging with energy detectors using ternary preamble sequences,” in Proc. IEEEWireless Commun. and Networking Conf., vol. 2, Las Vegas, NE, USA, Apr 2006, pp. 872–877.
Z. Tian and G. B. Giannakis, “A GLRT approach to data-aided timing acquisition in UWB radios-Part I: Algorithms,”IEEE Trans. Wireless Commun., vol. 4, no. 6, pp. 1536–1576, Nov. 2005.
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Selected references
C. Mazzucco, U. Spagnolini, and G. Mulas, “A ranging technique for UWB indoor channel based on power delay profileanalysis,” in Proc. IEEE Semiannual Veh. Technol. Conf., vol. 5, Milan, ITALY, May 2004, pp. 2595–2599.
L. Yang and G. B. Giannakis, “Timing ultra-wideband signals with dirty templates,” IEEE Trans. Commun., vol. 53, no. 11, pp. 1952–1963, Nov. 2005.
C. Gentile and A. Kik, “A comprehensive evaluation of indoor ranging using ultra-wideband technology,” EURASIP J.Wireless Commun. and Networking, vol. 2007, no. 1, pp. 12–12, 2007.
D. Dardari, Y. Karisan, S. Gezici, A. A. D’Amico, and U. Mengali, “Performance limits on ranging with cognitive radio,”in International Workshop on Synergies in Communicaions and Localization (SyCoLo 2009), Dresden, Germany, June 2009.
D. Dardari, “Pseudo-random active UWB reflectors for accurate ranging,” IEEE Commun. Lett., vol. 8, no. 10, pp. 608–610, Oct 2004.
D. B. Jourdan, D. Dardari, and M. Z. Win, “Position error bound for UWB localization in dense cluttered environments,”IEEE Trans. Aerosp. Electron. Syst., vol. 44, no. 2, pp. 613–628, Apr. 2008.
E. Paolini, A. Giorgetti, M. Chiani, R. Minutolo and M. Montanari, “Localization Capability of Cooperative Anti-intruder Radar Systems,” EURASIP Journal on Advances in Signal Processing, (Special Issue on Cooperative Localization in Wireless
Ad Hoc and Sensor Networks), vol. 2008, Article ID 726854, 14 pages, 2008
A. Giorgetti, M. Chiani, and D. Dardari, “Coexistence issues in cognitive radios based on ultra-wide bandwidth systems,” in 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM 2006, MykonosIsland, GREECE, June 2006, pp. 1–5.
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Special thanks
O. Andrisano (University of Bologna, Italy)M. Chiani (University of Bologna, Italy)C-C. Chong (DoCoMo Labs, USA)C. Falsi (University of Florence, Italy)U. Ferner (MIT, USA)S. Gezici (Bilkent University, Turkey)A. Giorgetti (University of Bologna, Italy)J. Lien (JPL, Nasa, USA)D. Jourdan (Athera, Washington, USA)L. Mucchi (University of Florence, Italy)M. Z. Win (MIT, USA)
In addition, thanks to N. Decarli, T. Pavani, R. Soloperto for their support during the measurement campaign