development of a context-aware vector-based high

ITM 2011, Session C4, San Diego, CA, 24-26 January 2011 1/13

Development of a Context-Aware Vector-Based

High-Sensitivity GNSS Software Receiver

Tao Lin, Cillian O’Driscoll and Gérard Lachapelle

Position Location And Navigation Group

Department of Geomatics Engineering

Schulich School of Engineering

University of Calgary

BIOGRAPHY

Tao Lin is a Ph.D. candidate in the PLAN Group of the

Department of Geomatics Engineering at the University

of Calgary. He received his BSc. from the same

department in May 2008. His research interests include

the fields of GNSS software receiver design, digital signal

processing, satellite-based navigation, inertial navigation

and ground-based wireless location.

Dr. Cillian O’Driscoll received his Ph.D. in 2007 from

the Department of Electrical and Electronic Engineering,

University College Cork. From 2007 to 2010 he was a

senior research engineer in the Position, Location and

Navigation (PLAN) group at the Department of

Geomatics Engineering in the University of Calgary. His

research interests are in the area of software receivers for

GNSS, particularly in relation to weak signal acquisition

and ultra-tight GPS/INS integration. He is currently a

post-doctoral fellow with the Joint Research Centre of the

European Commission.

Dr. Gérard Lachapelle is a Professor of Geomatics

Engineering at the University of Calgary where he is

responsible for teaching and research related to location,

positioning, and navigation. He has been involved with

GPS developments and applications since 1980. He has

held a Canada Research Chair in wireless location since

2001.

ABSTRACT

In a standard GNSS scalar-based receiver, GNSS signals

are usually processed on a satellite-by-satellite basis using

scalar-based tracking loops. In contrast, a vector-based

based receiver combines the signal processing and the

navigation solution into one step so that one tracking or

processing channel can aid other channels via the

navigation state; thus generally it has better sensitivity

over a scalar-based receiver. However the gain due to the

inter-channel aiding in a vector-based receiver is

negligible for indoor applications. In addition to vector-

based tracking, longer coherent integrations are needed

for indoor navigation. Although extending the coherent

integration time for conventional standard tracking loops

(DLL/FLL/PLL) and Kalman filter based tracking loops

is possible, the robustness of the carrier phase tracking in

these tracking loops remains an issue. In this paper, a

combined approach of the block processing and

centralized vector-based tracking is utilized for robust

indoor navigation. A context-aware approach is used to

optimize the processing load of the receiver and the

measurement weighting to provide seamless outdoor-

indoor navigation. Cascaded Kalman filter vector-based

tracking is used under open-sky conditions; block

processing and centralized vector processing is enabled

when the signal power drops, signal fading level

increases, or the Kalman filter tracking loops have

difficulty to maintain lock. The algorithms,

implementation details, and performance of the proposed

context-aware high-sensitivity GNSS software receiver

and its ultra-tight version are presented in this paper.

Based on the results shown, the proposed receivers have

acceptable robustness and accuracy for indoor navigation.

INTRODUCTION

The optimal choice of processing strategy and parameters

in a GNSS receiver is a function of many factors: the

strength of the signal, the Line-Of-Sight (LOS) dynamics,

and the signal fading level. All these factors can be

categorized into signal/channel context (signal strength

and fading level) and motion context (dynamics due to

motion or clock instability). There is, therefore, a desire to

develop a new GNSS receiver architecture that is able to

determine the channel and motion contexts and adjust its

processing strategies and parameters accordingly. Herein,

such a receiver is referred to as context-aware.

GNSS signals are usually processed on a satellite-by-

satellite basis using scalar-based tracking loops. The

benefits of scalar-based tracking are the relative ease of

implementation and a level of robustness that is gained by

not having one tracking channel corrupt another tracking


channel. However, the fact that the signals are related via

the receiver’s position and velocity is completely ignored.

In contrast, vector-based tracking combines the signal

processing and the navigation solution into one step so

that one tracking channel can aid other channels via the

estimated receiver’s position, velocity, clock offset, and

clock drift (Petovello et al 2008); thus, generally it has

better sensitivity over scalar-based tracking. This

sensitivity gain depends on the actual satellite geometry.

However, in a deep indoor or urban environment where a

30 dB attenuation level for all satellites in view is possible

(Watson 2003), the gain due to the inter-channel aiding in

vector-based tracking is negligible, since apparently there

is no strong signal in this case (Pany 2010). In addition to

attenuation, multipath fading will cause loss of lock in

conventional closed-loop signal tracking and errors in

navigation solution. To improve the sensitivity and

robustness of vector-based tracking loops, a larger

sensitivity gain from signal integration (i.e. coherent

integration) and better multipath fading resistance are

needed.

In order to provide seamless outdoor-indoor navigation, a

context-aware vector-based high-sensitivity receiver and

its ultra-tight version are proposed in this research. The

focus in this paper is the practical implementation and

performance evaluation of the proposed receivers. This

paper begins with a review of existing GNSS signal

processing strategies and receiver architectures. The

concept of context-aware processing is then explained.

After that, the architectures and implementation of the

proposed context-aware vector-based high sensitivity

receiver and its ultra-tight version are presented. The field

test results are finally presented and analyzed.

HIGH SENSITIVITY PROCESSING

In this section, several major channel processing

strategies and receiver architectures are introduced. Their

roles in GNSS high sensitivity processing are discussed.

Channel Processing Strategies

In this section, three channel processing strategies,

namely standard tracking, Kalman filter tracking, and

block processing, are briefly reviewed and compared.

Standard tracking is the most basic but commonly used in

GNSS receiver design communities and industries. Most

standard tracking loops were designed in the analog

domain first and then transformed into the digital domain

by the bilinear transform. As shown by Kazemi (2009),

the standard tracking loop needs to be re-designed in the

digital domain in order to extend its coherent integration

time over 100 ms. The processing flow of standard

tracking is shown in Figure 1. The correlator outputs

generated from the Doppler Removal and Correlation

(DRC) module are processed by discriminators and loop

filters to provide the updated signal parameter estimates.

The updated signal parameters are then used to generate

the local replicas for DRC process in the next epoch.

Figure 1 Architecture of a standard tracking loop

Kalman filter tracking is another option for signal

tracking. Although it is well known that a Kalman filter

tracking loop in steady state is equivalent to a standard

tracking loop, the Kalman filter can be used as a tool for

designing an optimal tracking loop based on the actual

equipment used (e.g. oscillators). Another benefit of a

Kalman filter tracking loop is that it is designed directly

in the digital domain; therefore, the instability issue for

standard tracking loops is not an issue for Kalman filter

track loops (Pany 2010).

Figure 2 Architecture of a Kalman filter tracking loop

Although standard tracking loops, particularly PLLs and

Kalman filter tracking loops, have been modified or

enhanced for weak signal tracking for indoor or urban

applications, their robustness remains an issue for indoor

navigation. Both standard tracking and Kalman filter

tracking are limited by several factors. First of all, the

Gaussian channel assumptions in both tracking loops are

not realistic. The real GNSS indoor signals are not only

weak but faded. The fluctuation of the signal strength due

to multipath fading has to be addressed. Secondly, better

performance cannot be guaranteed by simply increasing

coherent integration time. O’Driscoll et al (2010) have

shown that longer coherent integration time does not

guarantee to give better tracking performance for a

Kalman filter tracking loop. In fact, this is true for any

tracking loop due to the conflict between the longer

coherent integration time and the higher loop update rate.

Thirdly, the linear regions of discriminators or the


equivalent linear regions of these two tracking schemes

are too narrow, because of the very limited number of

correlators used. Finally, the signal carrier phase is too

difficult to track indoors due to noise and multipath

fading. Performing carrier phase tracking in these two

tracking schemes leads to frequent loss-of-lock.

Compared to the two tracking schemes above, block

processing is a very different approach. In standard

tracking and Kalman filter tracking, the local signal

replicas are fully synchronized to the incoming signals if

signals can be tracked properly. In block processing, the

local replicas are not fully synchronized to the incoming

signals, but the differences between local replicas and the

incoming signals are estimated from a grid of correlators.

In other words, the tracking errors are significantly

different from zero. This is similar to the centralized deep

coupling shown in Pany et al (2005). As shown by Van

Graas et al (2008), it is possible to use the signal

parameters estimated from the grid of correlators to

directly control local signal generator. However these

unfiltered estimates are much noisier than those from

channel loop filters; therefore, it is better to filter these

estimates by the navigation solution. Because of the

elimination of the local channel filter, the issues of loop

filter stability and the conflict between longer integration

time and higher loop update do not exist in block

processing. Therefore, as shown by Van Graas et al

(2008), block processing has better robustness compared

to standard tracking and Kalman filter tracking.

Figure 3 Architecture of block processing

Receiver Architectures

In general, GNSS receiver architectures can be

categorized into scalar-based receivers and vector-based

receivers. As shown below, the major difference between

a vector-based receiver and a scalar-based receiver is the

navigation solution feedback to each tracking channel.

This enables the inter-channel aiding among the channels

so that strong signals can help tracking weak signals.

Vector-based receivers can be further divided into two

groups, cascaded vector-based receivers and centralized

vector-based receivers. For centralized vector-based

receivers, the local tracking loop for each channel is open.

The discriminator outputs are directly fed into the

navigation solution without any filtering (Pany et al

2005). In contrast to centralized vector-based receivers,

the local tracking loops for each channel are still closed in

cascaded vector-based receivers (Petovello et al 2006).

Figure 4 Architecture of a scalar receiver

Figure 5 Architecture of a vector receiver

An ultra-tight receiver can be considered as a vector-

based receiver integrated with an IMU. From a GNSS

receiver architecture point of view, they are more or less

the same. The only difference between them is the

navigation solution. A GNSS-only solution is used in a

vector-based receiver, while a GNSS/INS integrated

solution is used in an ultra-tight receiver. The integrated

solution has a higher output rate and generally higher

accuracy than the GNSS only solution especially when

there are signal blockages. This will provide more

accurate/robust predicted pseudoranges (PSRs) and

pseudorange rates (PSRRs) for each channel in the

navigation feedback compared to the one in a vector-

based receiver. Also longer coherent integration time and

smaller loop bandwidth for the cascaded approach are

possible due to the LOS dynamic compensation by the

integrated navigation solution at a high output rate

(Petovello et al 2008, Pany et al 2009).


As discussed in the previous section, the outputs from

block processing could be too noisy to directly set the

local signal generator. The centralized vector-based

receiver architecture provides a chance to filter the block

processing output through the navigation solution.

Therefore, the vector-based receiver architecture and its

extension, ultra-tight receiver architecture, are used for

the proposed context-aware high-sensitivity receivers in

this paper.

Figure 6 Architecture of an ultra-tight receiver

CONTEXT-AWARE PROCESSING

From the discussion above, the combination of the block

processing strategy and the vector-based or the ultra-tight

receiver architecture will be a robust solution for indoor

navigation. However, the block processing strategy may

not be an ideal solution for many open-sky applications

(i.e. high precision survey). With the same coherent

integration time, the outputs from block processing are

noisier than those from standard or Kalman filter tracking.

The computation load is another disadvantage of block

processing due to a large amount of correlators. In short,

block processing is a suitable candidate for indoor

navigation if long coherent and non-coherent integrations

are used; however, it should be used only when it is

needed. Therefore, the receiver should be able to

determine when to enable the high sensitivity mode. The

navigation solution can benefit from the context-aware

processing as well, because the navigation solution for

outdoor applications and indoor applications could be

different in terms of the types of measurements, the

measurement weighting methods, and even the

architectures.

Context can be categorized as channel context (e.g. indoor

vs. outdoor) and motion context (e.g. static vs. kinematic).

The motion context here simply refers to the dynamics

due to satellite and user motion. Because velocity and

acceleration can be used to describe motion, the motion

context can be detected or characterized by the

GNSS/INS integrated navigation solution or even the

GNSS only navigation solution. The benefits of knowing

the motion context are LOS dynamics compensation for

longer coherent integration and/or narrower loop

bandwidth, and multipath fading characterization.

In contrast to motion context, channel context is more

challenging because GNSS signals are already weak in

open-sky compared to most communication signals. The

most common metrics for channel context detection are

listed below.

Table 1 Metrics for Context-Aware Detection

Metrics Characteristics

PLI Phase-locked indicator

FLI Frequency-locked indicator

C/N0 Signal strength level

Rician K-Factor Signal fading level

Chip Shape Chip shape of composite signals

Correlation Correlation shape of composite signals

Residuals Measurement residuals

Residuals depend on the number of satellites in-view and

the navigation solution at the previous epoch. The use of

residuals for context-aware detection will be investigated

in the future. Phase-Locked Indicator (PLI) and

Frequency-Locked Indicator (FLI) are indicators of how

well the phase and frequency are being tracked. They do

not have physical meaning regarding the types of

channels (i.e. Gaussian, Rician or Rayleigh) (Lin et al

2010). Chip shape and correlation shape in the code phase

domain can be used to detect and estimate the distortion

due to multipath as shown by Weil (1995), Jones et al

(2004), Fenton & Jones (2005), Weil (2007), and Lin et al

(2010) for outdoor applications. However, these

techniques require phase-lock, which are not suitable for

indoor applications since phase-lock are barely possible

for indoor signals (Lin et al 2010). C/N0 measures the

signal strength of the receiver signals. It is also an

indicator for the attenuation level of the received signals.

The Rician K-Factor measures the fading level of the

received signals. It can be used to determine the types of

propagation channels. Lin et al (2010) provide details of

the implementation of various Rician K-Factor estimators

and their performance/compatibility for GNSS signal

processing. The moment based Rician K-Factor

estimators were shown to be the most suitable candidates

for GNSS signal processing (Lin et al 2010).

The results below demonstrate the channel context

detection base on Rician K-Factor and C/N0 estimates

with different integration times. The GPS data used here

was collected in an experiment in a typical North


American wooden house. The antenna was initially held

by a pedestrian outdoor. The pedestrian with the antenna

first remained stationary for about 60 seconds. Then, the

antenna was moved into the first floor of the house, down

to the basement, back outdoors for a while, then moved

back to the first floor of the house finally. ‘Moment1,2,1st’,

‘Moment1,2,2nd’, and ‘Moment2,4’ are three Rician K-

Factor estimators introduced in Lin et al (2010).

‘Moment2,4’ refers to the Rician K-Factor estimator with

2nd

and 4th

moments. ‘Moment1,2,1st’ and ‘Moment1,2,2nd’

refer to the Rician K-Factor estimators with 1st and 2

nd

moments using a 1st order and a 2

nd order polynomial

approximation. More details can be found in Lin et al

(2010). It can be observed that C/N0 indicates the

attenuation level while Rician K-Factor indicates the

signal fading level. These two quantities can be used to

effectively detect indoor and outdoor channels.

Figure 7 C/N0 values with 100 ms coherent

integrations

Figure 8 Rician K-Factor values with 100 ms coherent

integrations

Figure 9 C/N0 values with 500 ms coherent

integrations

Figure 10 Rician K-Factor values with 500 ms

coherent integrations

As shown by Van Graas et al (2008) and Lin et al (2010),

multipath signals can be separated from LOS signals in

the frequency domain if coherent integration time is long

enough. This concept is illustrated below with real indoor

GPS signals using the same data set for the results above.

The coherent integration of 1 s was used in this case. As

shown in the figures blow, multiple peaks show up at

three consecutive epochs. Because data bits have been

perfectly wiped off with external data aiding in this case,

it can be concluded that some or all of these peaks are due

to multipath signals. More details can be found in Lin et

al (2010). Applying this technique for multipath

mitigation will be investigated in the future.


Figure 11 Correlations at 412808.6094 s



CONTEXT-AWARE HIGH SENSITIVITY

RECEIVERS

In this section, the proposed vector-based and ultra-tight

high sensitivity receivers are first introduced. The special

features in these two receivers are then presented.

The architecture of a context-aware vector-based high-

sensitivity receiver is shown in Figure 14 while its ultra-

tight version is shown in Figure 15. These two

architectures have been implemented in the GNSS

Software Navigation Receiver (GSNRxTM

) developed at

the University of Calgary (O’Driscoll et al 2009).

Figure 14 Architecture of GSNRx-hsTM

Figure 15 Architecture of GSNRx-hs-utTM

The proposed context-aware, vector-based high-

sensitivity receiver, GSNRx-hsTM

, and its ultra-tight

version, GSNRx-hs-utTM

, have a few noticeable

differences to the standard vector-based receiver

(GSNRx-vbTM

) and the standard ultra-tight receiver

(GSNRx-utTM

). GSNRx-hsTM

and GSNRx-hs-utTM

have

two operation modes: the standard mode and the high-

sensitivity mode. In the standard mode, a Kalman filter

tracking strategy is active; and the cascaded vector-

tracking is utilized. This is exactly the same as the

standard vector tracking and ultra-tight tracking in

GSNRx-vbTM

and GSNRx-utTM

.

In the high-sensitivity mode, the block processing strategy

is active. The outputs from block processing are used for

generating the pseudorange and pseudorange rate


measurements for the navigation solution. The local

signal generators are then updated by the filtered

pseudorange and pseudorange rate values from the

navigation filter. This is the same as the centralized vector

tracking discussed in Pany et al (2005). External data bit

aiding is used to permit long coherent integration times

for noise and multipath suppression, while external

ephemeris is used for navigation solution. Because of the

larger amount of correlators needed in block processing,

FFT parallel-frequency-based correlation method is used

to generate correlation/accumulation more efficiently in

the frequency domain. The basic idea for this algorithm is

to partially coherent integrate the correlator output (i.e. 1

ms output) at the Doppler frequency estimated at the

previous epoch up to the partial coherent integration time

(i.e. 10 ms), which defines the attenuation pattern of the

correlation function. These partial coherent integration

sums for each code bin are stored in an array. The FFT is

finally applied to these arrays to obtain the final two

dimensional correlation output. More details on FFT

parallel-frequency-based correlation can be found in Ma

et al (2011).

Another key feature of the high sensitivity processing

(block processing) in the proposed receivers is that block

processing can work in the signal synchronous mode or

the receiver synchronous mode. These two modes are

described below. The navigation measurements

(pseudorange, pseudorange rate, and carrier phase)

generation and signal tracking processes typically work

asynchronously with each other. As well, the tracking of

each satellite signal is asynchronous to other signals

because the code phases for different signals are not the

same at the same epoch (Pany et al 2005, O’Driscoll et al

2010). For standard tracking and Kalman filter tracking,

this is not an issue, since the local signal replicas are fully

synchronized to the incoming signals if tracking works

properly. In other words, the navigation measurements

can be generated from the local signal replicas at every

measurement epoch (i.e. every 1 s for 1 Hz measurement

rate). As mentioned above, block processing does not

track the incoming signals closely; therefore, the offset

between the local replicas and the incoming signals need

to be not only estimated at the channel update epochs (the

epochs for estimating the offsets) but also propagated

from the channel update epochs to the measurement

epochs for proper measurement generation. If the channel

update epochs for each channel are synchronous to their

signal time frames and asynchronous to the receiver time

fame (i.e. channel update epochs are asynchronous to the

measurement epochs), this process is a signal

synchronous process. Signal synchronous processes are

typically used in most GNSS receivers to avoid the

change of navigation data bits occurring during the

correlation or coherent integration process because all

signals being tracked are naturally asynchronous. If the

channel update epochs for each channel are asynchronous

to their signal time fames but synchronous to the receiver

time frame, this process is a receiver synchronous

process. Receiver synchronous processes are generally

not used if data bit aiding is not available. Because of

external data bit aiding, both signal synchronous and

receiver synchronous processes are available in GSNRx-

hsTM

and GSNRx-hs-utTM

.

For signal synchronous processes, the propagation of the

signal parameter offsets estimated at channel update

epochs is required. Depending on the time gap between

the channel update epoch and the measurement epoch,

such propagation may not be accurate enough if higher

order signal parameters (i.e. frequency rate) is not

available. For receiver synchronous processes, this is not

an issue since the signal parameters are estimated at the

same epochs as that of measurement generation.

Therefore, receiver synchronous processes may provide

measurements with better quality depending on the

models used.

In contrast to the traditional elevation-based weight

scheme, the measurement weightings in the proposed

receivers are based on the thermal noise jitter and other

error sources. The thermal noise jitter is a function of

channel processing strategies, filter bandwidths, and

coherent integration times. For high sensitivity mode, the

pseudorange measurement weightings are intentionally

scaled down since pseudorange measurements are more

susceptible to multipath than carrier Doppler (Petovello et

al 2003).

FIELD TEST RESULTS

An experiment in a typical North American wooden

house was conducted to evaluate performance of the

proposed receiver architectures. In this experiment, an

antenna was mounted on an aluminum frame carried by a

pedestrian (see Figure 18). The GPS IF data was collected

with a National Instrument (NI) RF front-end. A

NovAtel’s SPAN HG1700TM

system, which includes a

L1/L2 survey grade GNSS receiver and a tactical grade

IMU (HG1700), was placed in the aluminum frame for

the reference solution. A lower grade IMU, CPT, which is

comprised of FOG gyros and MEMS accelerometers, was

also used in the experiment for the high sensitivity ultra-

tight solution. The specifications of these two IMUs are

provided in Table 2 (NovAtel 2009, 2010b). The

performance comparison of HG1700 and CPT IMUs

during GPS signal outages can be found in NovAtel

(2010a).


Table 2 IMU specifications

IMU HG1700 CPT

Gyro Bias (deg/hr) 1.0 20.0

Gyro Bias Stability (deg/hr) N/A 1.0

Gyro Scale Factor (ppm) 150 1500

Accelerometer Bias (mg) 1.0 50.0

Accelerometer Bias stability (mg) N/A 0.75

Accelerometer Scale Factor (ppm) 300 4000

The pedestrian was walking from outdoors to indoors then

back to the original starting point outdoors. The sky-plot

is shown in Figure 16. The equipment and the field test

environment are shown in Figure 17 and Figure 18.

Figure 16 Sky plot

Figure 17 Environment of the field test

Figure 18 Hardware for the field test

The estimated C/N0 and Rician K-Factor values from

GSNRx-hsTM

are plotted in Figure 19 and Figure 20. As

expected, the mean values of the estimated C/N0 are lower

while the variations of the estimated C/N0 are larger

compared to the outdoor values. These variations are

captured by the estimated Rician K-Factor as well. More

interestingly, when the pedestrian was approaching the

building or just leaving the building, significant

fluctuations can be observed on the corresponding C/N0

values because of the multipath fading and the partial

signal blockage from the building. These are again can be

more readily visualized from the estimated Rician K-

Factor values. In fact, this proves that Rician K-Factor

can be used with C/N0 for context detection.


Figure 19 Estimated C/N0 values from GSNRx-hsTM

Figure 20 Estimated Rician K-Factor values from

GSNRx-hsTM

The reference solution used in this test was generated

from the NovAtel’s Inertial ExplorerTM

software package

and the SPAN-HG1700TM

GPS/INS measurements. The

RTK GPS/INS tightly couple solution with forward-

backward smoothing was used as the reference. The

estimated standard deviations of the reference position

and velocity over time are plotted in Figure 21 and Figure

22. These values are provided by the Inertial ExplorerTM

software package. Because of the tactical grade IMU

HG1700, the reference solution can be maintained at the

sub-metre accuracy while the pedestrian was walking

indoors. This accuracy is sufficient for reference purposes

in this paper.

Figure 21 Estimated position accuracy of the reference

solution

Figure 22 Estimated velocity accuracy of the reference

solution

As mentioned in the previous section, signal synchronous

and receiver synchronous options are available in

GSNRx-hsTM

and GSNRx-hs-utTM

. In order to evaluate

the measurement quality from these two processes, the

block processing strategy in GSNRx-hsTM

was enabled

when navigation frame synchronization was achieved so

that the data bit aiding can be used for bit wipe-off. 500

ms coherent integrations were used in both cases. In

Figure 23 and Figure 24, the position errors and the

velocity errors for the signal synchronous and the receiver

synchronous processes are plotted. The position and

velocity estimates from the receiver synchronous

processing are slightly more accurate that those from the

signal synchronous processing, especially when the

dynamics was relatively high. This may be because the

model used for signal parameter offset propagation is sub-

optimal due to the lack of higher order signal information.

For the receiver synchronous processing, this sub-optimal

propagation is omitted; thus, the errors are smaller

compared to the signal synchronous process. However,

the difference between the solutions from these two

processes is small because only 500 ms of coherent

integration was used here. If a longer coherent integration


was used, the benefit of the receiver synchronous process

may be more pronounced.

Figure 23 Comparison of position errors

Figure 24 Comparison of velocity errors

Based on the C/N0 and Rician K-Factor estimates, the

change of the channel context can be detected. This is

also the point for switching processing strategies. The

data was re-processed with GSNRx-hsTM

to evaluate the

performance of the context-aware solution. The high

sensitivity mode in GSNRx-hsTM

is enabled only when the

C/N0 and Rician K-Factor estimates are below the pre-

defined thresholds of 32 dB-Hz and 10 dB respectively.

More tests will be conducted to evaluate the impact of the

thresholds in the future, but this seems to be an effective

engineering design choice to the authors. The FFT-

parallel-frequency method was used for long coherent

integrations. The partial coherent integration used in the

process here was 10 ms and the total coherent integration

time was 500 ms. The signal synchronous process was

used.

The estimated trajectories from GSNRx-hsTM

and the

reference solution are shown in Figure 25. The position

and velocity errors are shown in Figure 26 and Figure 27.

The estimated trajectory from the proposed high

sensitivity receiver is reasonably close to the one from the

reference solution. The position errors are acceptable in

general for this signal degraded environment while the

velocity errors are a bit larger than expected. This could

be the side-effect of the heavy filtering in the navigation

solution.

Figure 25 Estimated trajectories from GSNRx-hsTM

and the reference solution

Figure 26 Position solution of GSNRx-hsTM


Figure 27 Velocity solution of GSNRx-hsTM

The data set was processed with the proposed high-

sensitivity ultra-tight receiver, GSNRx-hs-utTM

, as well

with the same parameters. A CPT IMU, which comprises

three FOG gyros and three MEMS accelerometers, was

used for the ultra-tight solution. The mechanization

algorithm is the traditional strapdown algorithm used in

Petovello et al (2008). The estimated trajectory from the

ultra-tight solution is compared with the one from the

reference solution in Figure 28. The position and velocity

errors are shown in Figure 29 and Figure 30.

Figure 28 Estimated trajectories from GSNRx-hs-utTM

and the reference solution

Figure 29 Position solution of GSNRx-hs-utTM

Figure 30 Velocity solution of GSNRx-hs-utTM

The statistics of the position and velocity errors for these

two solutions are summarized in Table 3 and Table 4.

Compared to the results of the GPS only solution, the

results from the ultra-tight solution are more accurate

especially when larger motion or maneuvers occur during

the test. This is because the pedestrian motion was

measured by the CPT IMU at 100 Hz in the ultra-tight

solution while it can be only predicted by the dynamic

model in the Kalman filter used by the GPS only solution.

The heavy filtering in the Kalman filter solution made this

more pronounced. However, considering the cost and the

power saving associated with the absence of IMU’s, the

GPS only solution from GSNRx-hsTM

is acceptable for

many low cost indoor navigation applications.


Table 3 Position accuracy

Position Error (m)

Northing Easting Vertical

Max Mean Max Mean Max Mean

GSNRx-

hsTM

2.54 -0.65 2.05 -0.67 11.25 -1.69

GSNRx-

hs-utTM

2.24 -0.49 1.65 -0.52 7.42 -0.82

Table 4 Velocity accuracy

Velocity Error (m/s)

Northing Easting Vertical

Max Mean Max Mean Max Mean

GSNRx-

hsTM

0.57 0.01 0.63 -0.02 0.47 0.03

GSNRx-

hs-utTM

0.47 0.00 0.41 -0.00 0.22 -0.01

CONCLUSIONS

In this paper, a context-aware, vector-based high

sensitivity receiver and its ultra-tight version were

proposed to provide seamless outdoor-indoor navigation.

The proposed receiver architectures have been

implemented on a GNSS software receiver platform and

tested with real GPS indoor signals. High-sensitivity and

context-aware processing algorithms were introduced.

The field test results demonstrated that the proposed

receivers can provide metre-level solution for a typical

North American wooden house type of indoor

environments.

ACKNOWLEDGMENTS

The assistance of Martin Ma in the development of the

software, and Billy Chan and Behnam Aminian for the

data collection is greatly appreciated.

This work was conducted with funding provided by

iCORE, part of Alberta Innovates – Technology Future.

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