Transcript Interferometric ranging: a new paradigm for sensor

Institute for Software Integrated Systems
Vanderbilt University
Wireless Sensor Net Research
@
ISIS
Akos Ledeczi
Senior Research Scientist
Outline
 Countersniper system:
 WSN-based static deployment
 Soldier-wearable system for sniper localization and weapon classification
 Sensor node localization:
 Radio Interferometric Positioning
 Radio Interferometric Tracking
 Doppler-shift based Tracking
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Countersniper System
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Patented Sensor Fusion
Shot #1 @ (x1,y1,T1)
t3
d3
Shot #2 @ (x2,y2,T2)
f(x,y)
t1
d1
?
Echo #1 @ (x3,y3,T1)
d4
t4
d2
t2
t3 – d3/v
t4 – d4/v
t2 – d2/v
sliding window
time
3
Shot time estimate T
t1 – d1/v
0
1
f(x,y) = [max number of ticks in window] = 3
Identifies echoes and resolves multiple simultaneous shots
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Experiments at McKenna MOUT site at
Ft. Benning
 Sep 2003: Baseline system
 Apr 2004: Multishot resolution
B1
NORTH
Church
 60 motes covered a 100x40m area
 Network diameter: ~7 hops
 Used blanks and Short Range Training
Ammunition (SRTA)
 Hundreds of shots fired from ~40 different
locations
 Single shooter, operating in semiautomatic
and burst mode in 2003
 Up to four shooters and up to 10 shots per
second in 2004
 M-16, M-4, no sniper rifle
 Variety of shooter locations (bell tower, inside
buildings/windows, behind mailbox, behind
car, …) chosen to absorb acoustic energy,
have limited line of sight on sensor networks
 1 meter average 3D accuracy (0.6m in 2D)
 Hand placed motes on surveyed points
(sensor localization accuracy: ~ 0.3m)
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Shooter Localization
VIDEO
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DARPA IPTO ASSIST: Soldier-Wearable Shooter
Localization and Weapon Classification System
3-axis compass
Optional
laptop display
Microphones
Zigbee
&
Bluetooth
Bluetooth
PDA display
Zigbee
Muzzle blast
Shockwave
Zigbee
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Bluetooth
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Latest Sensor Board
 Detect TOA and AOA of acoustic shockwave
and muzzle blast using a single board
 New acoustic sensor board:
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4 acoustic channels w/ high-speed AD converters
FPGA for signal processing
3-axis digital compass
Bluetooth
LEDs for on-board display
MicaZ connectivity
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Architecture
 PC/PDA (Java/Ewe)
 User interface
 Local/central sensor fusion
 Location information from
external GPS
 Sensor Board (VHDL/assembly)
 Custom DSP IP cores (detection)
 Soft processor macros (digital
compass, debug & test interface)
 Communication bridge
 Shared memory paradigm
 Mote (nesC/TinyOS):
 Data sharing across nodes
 Time synchronization
 Application Configuration &
Management (from a central
point)
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Sensor Fusion
 Localization: Single sensor: simple
analytical formula to compute shooter
location based on Time of Arrival (ToA)
and Angle of Arrival (AoA) of both
shockwave and muzzle blast.
 Localization: Multi-sensor: all available
detections are utilized in a
multiresolution search of a discrete
multi-dimensional consistency function.
Consistency function specifies how
many observations agree on a given
point in space and time.
 Online caliber estimation based on
measured ballistic shockwave length
and miss distance given by the
computed trajectory estimate.
 Online weapon classification based on
estimated caliber and muzzle velocity
that is computed using the projectile
velocity over the sensor web and the
estimated range.
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Single Sensor Results
 Localization rate for single sensors:
 range < 130m: 42%
 Range < 80m: 61%
 Percentage of shots not localized by
at least one single sensors alone
(range < 150m): 13%
 Accuracy:
 0.9 degree in azimuth
 5 m in range
Blue dots: sensors
Black squares: targets
Black line: trajectory estimate
Black dot: shooter position estimate
White arrows: single sensor shooter estimates
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Multi-Sensor Results
Localization Results
 Independent evaluation by NIST at
Aberdeen
 Shots between 50 and 300 m w/ 6
different weapons (3 calibers)
 Trajectory was highly accurate
 Big range error at >200m was due to
a bug in the muzzle blast detection
 Caliber estimation was almost
perfect (rates are relative to
localized shots, not all shots).
Classification Results
Sensors located o surveyed points with practically no position error.
Manual orientation and then automatic calibration used. No mobility.
 Classification for 4 out of 6 six
weapons were excellent
 At longer ranges it started to
degrade as it needs range estimate,
i.e. muzzle blast detections
 M4 and M249 was too similar to
each other and the test was the first
time the system encountered these
weapons
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Radio Interferometric Localization
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12000m2 area
16 XSM motes on the ground
Minimum node distance 25m
3 anchor nodes
Took 50 minutes
Average loc error < 4cm
Maximum loc error 12cm
Maximum “range” 170m
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Ranging
dABCD modulo λ
λ
dABCD
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Interferometric range:
dABCD=dAD−dBD+dBC−dAC
Phase offset measurements: dABCD mod λ
(65cm < λ < 75cm)
Multiple measurements at different
frequencies
Wavelength ambiguity
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Multipath
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Ranging: minimum of discrepancy function
(RMS error)
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RF multipath may cause significant phase
error
Global minimum may not be the correct
solution
Solution: use interleaved ranging/localization
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Radio Interferometric Tracking
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Use N “infrastructure nodes” at known locations to track M moving nodes
using radio interferometric ranging
Tracked node is transmitter: more measurements, but single tracked object
Tracked node is receiver: less measurements, multiple tracked objects, but
less accurate
actual distances between the nodes change as the tracked object moves
1 ranging measurement (20 channels) takes significant time (0.5 sec)
so, the q-range dABCD changes significantly during the measurement period,
rendering the set of Diophantine-like equations incorrect
φ1CX = dAX – dBX mod λ1
...
φkCX = dAX – dBX mod λk
Solution:
- estimate the mobile object’s velocity and compensate for these errors
- to estimate the velocity, we measure Doppler shifts
- benefits:
- improved accuracy of localization
- We can compute the velocity vector of the moving target also
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Results: single tracked node
 Vanderbilt Football Stadium
 12 motes deployed at known
positions
 One extra node is tracked
 The tracked node and one other
are the transmitters, the rest are
receivers
 11 channels are measured, but only
4 consecutive ones are used at a
time in the sensor fusion
 No speed compensation
 Consistency function based
multiresolution search algorithm
running on the base station finds
location estimate
 Accuracy: <1m
 Update rate: ~1 per 3 seconds
 Max speed: ~3m/s
Note: Hard to establish ground truth
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Results: multiple tracked nodes
3 persons walking
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Vanderbilt Football Stadium
5 motes deployed at known positions
3 extra nodes are tracked
The tracked nodes are receivers
10 channels are measured and used
concurrently in the sensor fusion
Speed compensation measuring Doppler shift
Analytical solution
Accuracy: <1m
Update rate: ~1 per 4 seconds
Max speed: ~2m/s
Note: Even harder to establish ground truth
2 persons walking,
one with two nodes in outstretched hands
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ISIS+ORNL: Dirty Bomb Localization
Outside the window
Jumbotron: automatic camera feed
Jumbotron/Screen: Tracking info inside Google Earth
 Security is guard walking around the stadium with a cell-phone
connected radiation detector and an Crossbow XSM mote.
 His position is continuously tracked using a radio interferometric
technique running on the motes.
 A camera automatically tracks his position using the geolocation
info from the mote network.
 When the radiation level crosses a threshold the
detector sends an alarm and the
camera zooms in on the position.
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Dirty Bomb Localization
VIDEO
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Utilizing Doppler Effect
 Single receiver allows us to measure relative speed.
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Utilizing Doppler Effect
 Multiple receivers allow us to calculate location and
velocity of the tracked node.
Copyright © 2004-2007, Vanderbilt University
Can we Measure Doppler
Shifts?
Typ. freq
Dopp. Shift
(@ 1 m/s)
Acoustic signals
1-5 kHz
3-15 Hz
Radio signals (mica2)
433 MHz
1.3 Hz
Radio signals (telos)
2.4 GHz
8 Hz
Intriguing option: if we can utilize radio signals, no
extra HW is required
Solution: radio interfereometry
Copyright © 2004-2007, Vanderbilt University
Measuring Doppler shift
We use radio interferometry to measure Doppler
frequency shifts with 0.2 Hz accuracy.
430MHz
430MHz+300
Hz
T
A
Si
300Hz + Δfi,T
• 2 nodes T, A transmit sine
waves @430 MHz
fT, fA
• Node Si receives interference
signal (in stationary case)
fi = fT – fA
• T is moving, fi is Doppler shifted
fi = fT – fA + Δfi,T
(one problem: we don’t know the
value fT-fA accurately)
Beat frequency is estimated using the RSSI signal.
Copyright © 2004-2007, Vanderbilt University
Formalization
We want to calculate both location and velocity
of node T from the measured Doppler shifts.
Unknowns:
• Location, velocity of T, and fT-fA
x=(x,y,vx,vy,f^)
Knowns (constraints):
• Locations (xi,yi) of nodes Si
• Doppler shifted frequencies fi
c=(f1,…,fn)
Function H(x)=c:
f4 = fT – fA + Δf4
= fT – fA + v4/λT
Non-linear system of equations!
Copyright © 2004-2007, Vanderbilt University
Tracking Algorithm
Infrastructure nodes record Doppler
shifted beat frequency.
Doppler shifted frequencies
Calculate location and velocity using
Kalman filter.
Extended
Kalman filter
Location & Velocity
Maneuver
detection
Yes
Non-linear
least squares
No
Location
& Velocity
NLS Location
& Velocity
Update EKF
Updated Location
& Velocity
Run a simple maneuver detection
algorithm.
If maneuver is detected, calculate
NLS solution and update EKF state.
Show location on the screen.
Copyright © 2004-2007, Vanderbilt University
Experimental Evaluation
Vanderbilt football stadium
• 50 x 30 m area
• 9 infrastructure XSM nodes
• 1 XSM mote tracked
• position fix in 1.5 seconds
Non-maneuvering case
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Copyright © 2004-2007, Vanderbilt University
Experimental Evaluation
Vanderbilt football stadium
• 50 x 30 m area
• 9 infrastructure XSM nodes
• 1 XSM mote tracked
• position fix in 1.5 seconds
Maneuvering case
Only some of the tracks are shown for clarity.
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Questions?
More information:
http://www.isis.vanderbilt.edu/projects/nest
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