See#through#Walls#with#Wi0Fijasleen/Courses/Fall15-635/slides/WiVi-Navaneet.pdf10/1/15 6...
Transcript of 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:
hcp://www.girdsystems.com/pdf/GIRD_Systems_Intro_to_MUSIC_ESPRIT.pdf
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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
hcp://www.girdsystems.com/pdf/GIRD_Systems_Intro_to_MUSIC_ESPRIT.pdf
MUSIC Algorithm Background (con<nued)
• All N sensors:
• a(𝜃) – “steering vector”
hcp://www.girdsystems.com/pdf/GIRD_Systems_Intro_to_MUSIC_ESPRIT.pdf
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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
hcp://www.girdsystems.com/pdf/GIRD_Systems_Intro_to_MUSIC_ESPRIT.pdf
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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
hcp://www.girdsystems.com/pdf/GIRD_Systems_Intro_to_MUSIC_ESPRIT.pdf
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