Interferometric ranging: a new paradigm for sensor
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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
Copyright © 2004-2007, Vanderbilt University
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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:
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
Copyright © 2004-2007, Vanderbilt University
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
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
Ranging: minimum of discrepancy function
(RMS error)
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
-
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
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.
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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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