see#through#walls#with#wi0fijasleen/courses/fall15-635/slides/wivi-navaneet.pdf10/1/15 6...

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See through Walls with Wi-‐Fi

Presented by Navaneet Galagali

Authors: Fadel Adib and Dina Katabi

Overview

• Goal: Detect and track moving objects behind a wall or closed door using Wi-‐Fi signals • Primary novelEes of the approach:

o Eliminate “flash effect” by MIMO nulling o ISAR technique to track moving objects

• ApplicaEons: Law enforcement, surveillance, gaming

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Wi-‐Vi (Wi-‐Fi Vision)

• A wireless device consisEng of three USRP N210 radios (two for transmiXng) connected to an external clock and LP0965 direcEonal antennas • Uses Wi-‐Fi OFDM signals in the ISM band (at 2.4 GHz) •  Two modes

o Track moving objects behind a wall o Gesture-‐interface for people to communicate messages from behind a wall

Flash Effect

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Flash Effect (con<nued)

• AcenuaEon of signal depends on the material of the wall and cross-‐secEon of the object •  In actuality, two-‐way acenuaEon occurs as the signal passes through the wall twice

Past work in tracking moving targets

•  Through-‐wall radar • Gesture-‐based interfaces (Xbox Kinect, Nintendo Wii MoEonPlus) •  Infrared/Thermal Imaging

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Through-‐wall radar

•  Track objects behind the wall via Eme domain or frequency domain • Require ultra-‐wide bandwidth (UWB) around 2 GHz – not feasible in a civilian seXng

Through-‐wall radar (con<nued)

• Other narrowband radar systems ignore the flash effect and use Doppler Shig to detect moving objects – only work in ideal scenarios (i.e., minimal obstrucEon) • One acempt using Wi-‐Fi signals required a transmicer and receiver inside the room clock synchronized to a receiver outside the room

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Gesture-‐based interfaces

• Requires line-‐of-‐sight (LoS) acEviEes and uses cameras or sensors placed on the body • Xbox Kinect, Nintendo Wii MoEonPlus

Infrared/Thermal Imaging

• Capture infrared/thermal energy reflected off object in LoS of sensor • Cannot see through walls because they have short wavelengths •  Infrared wavelength ~ 1013 Hz, 802.11n ~ 109

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Wi-‐Vi’s improvements

• NLoS (non-‐line-‐of-‐sight) scenarios •  Signals with longer wavelengths that are able to go through walls • No sensors on the target or devices inside the room • Requires a few MHz of bandwidth •  Eliminates the flash effect by MIMO interference nulling

Elimina<ng the flash effect •  IniEal Nulling – standard MIMO nulling • Power BoosEng – increase transmiced signal power •  IteraEve Nulling – null staEc object reflecEons again

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Ini<al Nulling

•  Transmit antennas send a known preamble ‘x’ • Receive antenna receives y1 = h1x and y2 = h2x • Compute channel esEmates ℎ ↓1  and ℎ ↓2  and obtain raEo p = − ℎ ↓1 /ℎ ↓2    • Both transmit antennas transmit concurrently, with perceived channel:

Power Boos<ng

•  Signals due to moving objects are not strong enough, so we increase the transmiced signal power • Because the channel is already nulled, the increase in power does not overwhelm the receiver’s ADC (analog to digital converter) • Overall result is improved SNR (signal to noise raEo) of objects behind the wall

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Itera<ve Nulling

• Power boosEng causes previously negligible staEc reflecEons to spike up, so we must null again • Challenge: Cannot separately esEmate channels from transmit antennas because only combined channel is received ager iniEal nulling • Removing iniEal nulling would saturate the ADC due to the power boosEng step •  Insight: Errors in channel esEmates are much smaller than channel esEmates themselves

Itera<ve Nulling (con<nued)

•  Assume and h2 esEmate is accurate (so ) and solve for ℎ ′↓1 :

ℎ↓𝑟𝑒𝑠 = ℎ↓1 − ℎ ↓1 

•  Assume the same for h1 and solve for ℎ ′↓2 :

•  Iterate between steps unEl h1 and h2 esEmates converge

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Final points on MIMO nulling

• Can be performed when objects are moving behind the wall or in front of the wall (as long as they are moving out of the view of the direcEonal antennas) • Algorithm provides a 42 dB mean reducEon in power, which removes the flash effect from solid wood doors, 6’’ hollow walls, and most indoor concrete walls

Tracking Mo<on in prior work

• Past systems used an antenna array •  Tracking the AoA in Eme tracks movement of the object

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Tracking Mo<on in prior work

•  Large antenna array is needed to obtain a narrow beam and good resoluEon •  Increasing length of the antenna decreases its footprint •  Each receive antenna would need corresponding transmit antennas for MIMO nulling, making it even bulkier

hcp://www.crisp.nus.edu.sg/~research/tutorial/mw.htm

Tracking Mo<on using ISAR

•  Treats the movement of the target as an antenna array •  Target takes AoA of signal as target moves •  Time samples received by Wi-‐Vi correspond to spaEal locaEons of the moving target • A technique used in mapping the surfaces of planets

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ISAR (Inverse Synthe<c Aperture Radar)

•  y[n] : Signal sample received by Wi-‐Vi at Eme n • 𝜃 : Angle between the line from human to Wi-‐Vi and the normal to the moEon • A[𝜃, n] : A funcEon that measures the signal along the spaEal direcEon 𝜃 at Eme n

ISAR (con<nued)

• h[n] : Received samples as a funcEon of Eme = n • h[n] = y[n]/x[n] • Antenna array of size w uses consecuEve channel measurements h[n]…h[n+w]

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ISAR (con<nued)

• 𝜆 – wavelength, Δ – spaEal separaEon between successive antennas in the array •  The value of 𝜃 that causes highest value of A[𝜃, n] is the direcEon of target movement

ISAR (con<nued)

• Δ = vT (distance = velocity * Eme), approximaEng v = 1 m/s (walking speed) in Wi-‐Vi •  Errors in value of ‘v’ overesEmate or underesEmate the direcEon of the target • With errors, Wi-‐Vi is able to track relaEve movement of the target, but cannot pinpoint exact locaEon

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ISAR output

•  Zero line represenEng DC – average energy from staEc elements •  Curved line with changing angle tracks target’s moEon •  [0-‐1.8 sec]: Person’s moEon and line from person to Wi-‐Vi are in same direcEon •  1.8 sec: Person crosses in front of Wi-‐Vi device •  [1.8-‐3 sec]: Person’s moEon and line from person to Wi-‐Vi are in opposite direcEon •  [3 onwards]: Person moves inward and towards the Wi-‐Vi device

Tracking Mul<ple Humans

• Received signal is a superposiEon of all the antenna arrays represenEng all moving targets •  Signal reflected off all humans is correlated in Eme and is not independent (they may interact with one another) • Apply smoothed MUSIC algorithm to disentangle signals

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MUSIC Algorithm Background •  Stands for MulEple Signal Classifier •  Super-‐resoluEon DOA (direcEon of arrival) algorithm • Applied only to narrowband signal sources – represented as complex sinusoids

hcp://www.girdsystems.com/pdf/GIRD_Systems_Intro_to_MUSIC_ESPRIT.pdf

Complex Sinusoids Background

•  Signal represented as a complex sinusoid:

• A real sinusoid is the sum of two complex sinusoids

• A delay of a sinusoid is a phase shig:


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Antenna Array Background

•  Signal s(t) hits the array at angle 𝜃 • At sensor 1, let received signal x1(t) = s(t) • Delay at sensor i is

• Received signal at sensor i is


MUSIC Algorithm Background (con<nued)

• All N sensors:

•  a(𝜃) – “steering vector”


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MUSIC Algorithm Background (con<nued)

•  Signal data model: X = AF + W o X – Received signals (in our paper this is denoted ‘h’) o A – Steering vectors for all source signals o F – Incident Signals o W – Noise

hcp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1143830

MUSIC Algorithm

• Compute correlaEon matrix: •  Eigen decomposiEon of R[n] gives the eigenvectors corresponding to the moving humans and DC line • ParEEon eigenvector matrix into signal space (US) and noise space (UN) • Key idea (1): Signal space and noise space are orthogonal • Key idea (2): Steering vector a(𝜃) is equal to the signal space •  Thus, a(𝜃)UN = 0


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MUSIC algorithm (con<nued)

• Power density is computed:

•  a(𝜃) -‐ steering vector consisEng of the terms • Whenever 𝜃 corresponds to the real signals, P(𝜃) shows a peak • Peak will indicate the angle of the signal


MUSIC Algorithm (con<nued)

•  Same formula:

• K – Total number of noise eigenvectors • w – number of sensors • 𝜔 -‐ angular wavenumber corresponding to 2𝜋/𝜆  where 𝜆 is the wavelength

•  For comparison:

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“Smoothed” MUSIC algorithm

• Compute w x w correlaEon matrix R[n]: •  “Smoothing” – ParEEon each array h of size w into subarrays of size w’ and compute correlaEon matrix R[n] for each of them

•  Sum up the different correlaEon matrices and then perform eigen decomposiEon

“Smoothed” MUSIC algorithm (con<nued)

• Benefit: De-‐correlates signals coming from different spaEal targets •  Taking overlapping subarrays of the same antenna array shigs reflecEons from other targets by different amounts, which helps to de-‐correlate them

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Detec<ng number of moving targets

•  SpaEal variance as a measure of the number of moving targets

•  SpaEal Centroid:

•  SpaEal Variance:

• Variance is averaged to return one number for the rest of the measurement

Spa<al variance thresholds

•  ProporEonal to # of targets so a training set is used to find the thresholds for 0, 1, 2, 3 humans

•  Adding more humans to a congested space doesn’t increase the spaEal variance as much as adding more humans to a less congested space

•  As a result, there is some inaccuracy as the number of humans increases

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Gesture-‐based communica<on

• Wi-‐Vi enables humans to communicate without a wireless device via simple gesture-‐based communicaEon • Gestures are encoded using ‘0’ and ‘1’ bits • Wi-‐Vi’s three imposed gesture condiEons:

o Must be composable: At the end of a ‘0’ or ‘1’ bit, human should be back in iniEal state

o Must be simple o Must be easy to detect and decode without the use of machine learning techniques

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Gesture Encoding

• Wi-‐Vi has adopted the following scheme for encoding gestures: o ‘0’ bit: A step forward followed by a step backward o ‘1’ bit: A step backward followed by a step forward

Gesture Decoding • Apply two matched filters to A’[𝜃, n] for the step forward and step backward • Matched filter: A linear filter that is designed to detect the presence of a waveform that is buried in addiEonal noise. • Apply standard peak detector to match the peaks/troughs to their corresponding bits

hcp://local.eleceng.uct.ac.za/courses/EEE3086F/notes/212-‐Matched_Filter_2up.pdf

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Gesture Decoding (con<nued)

Matched filter

Standard Peak detector

Gesture Recogni<on Performance

• Distance less than 5m: 100% • Distance between 6m and 7m: 93.75% • Distance at 8m: 75% • Distance greater than 9m: None

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Gesture Recogni<on Performance (con<nued)

• Glass, solid wooden doors, interior walls, concrete walls of limited thickness • Does not work with denser material (ex. Reinforced concrete)

Limita<ons

• Can only detect moving targets • Assumes a given velocity of moEon (delta = vT) in order to pinpoint the target • ResoluEon decreases as the number of moving targets increases and as the distance of the targets increases

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Conclusion

• Wi-‐Vi enables detecEon of moving targets behind a wall using Wi-‐Fi signals • Represents a form of Wi-‐Fi-‐based sensing and localizaEon and raises quesEons of user privacy and regulaEon around Wi-‐Fi signals • With becer hardware and improved nulling techniques, the resoluEon of the system will improve for greater distances and denser building materials

see#through#walls#with#wi0fijasleen/courses/fall15-635/slides/wivi-navaneet.pdf10/1/15 6...

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