Transcript hotemnets08-indoorloc

Towards Precise Indoor
RF Localization
Akos Ledeczi, Janos Sallai, Xenofon Koutsoukos, Peter Volgyesi
Vanderbilt University
Branislav Kusy
Stanford University
Miklos Maroti
University of Szeged, Hungary
Overview
•Objective – accurate indoor localization using radio interferometry (RI)
•Motivation – applications need location service, but GPS has many limitations
•Radio-interferometic ranging – how does it work ?
•Previous work – localization and tracking with RI andradio interferometric Doppler shifts
•Challenges – multipath, complex localization algorithms, long measurement duration
•Approach – lower carrier frequency, asymmetric architecture
•Work in progress – preliminary experimental results (time synchronization)
Motivation
Important applications
• require high accuracy localization
• ad-hoc deployable wireless sensors
• need to operate without human
intervention
Motivation
Important applications
Why radio-interferometry?
• require high accuracy localization
• ad-hoc deployable wireless sensors
• power constraints (lifetime of months on 2
AA batteries)
• need to operate without human
intervention
• GPS is often not applicable
• low cost – enables redundancy
Motivation
Important applications
Why radio-interferometry?
• require high accuracy localization
• ad-hoc deployable wireless sensors
• power constraints (lifetime of months on 2
AA batteries)
• need to operate without human
intervention
• GPS is often not applicable
• low cost – enables redundancy
Potentials of radio-interferometry
• Can be implemented with cheap HW
• More accurate than acoustic/ultrasonic/rf
TOF/TDOA
• Does not require line of sight
Motivation
Important applications
Why radio-interferometry?
• require high accuracy localization
• ad-hoc deployable wireless sensors
• power constraints (lifetime of months on 2
AA batteries)
• need to operate without human
intervention
• GPS is often not applicable
• low cost – enables redundancy
Potentials of radio-interferometry
• Can be implemented with cheap HW
• More accurate than acoustic/ultrasonic/rf
TOF/TDOA
• Does not require line of sight
Challenges
• Sensitive to RF multipath
• Measurements take a long time
• Localization algorithms are complex
Radio Interferometry
Interference
superposition of two waves (from one or two sources) resulting in new wave pattern
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Applications
 traditionally used in applied physics (geodesy, astronomy,…)
compute cross-correlation of a signal from a single source recorded by 2 observers
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Problem
sensor hardware has insufficient processing power to compute correlation online
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Solution
 two transmitters slightly out of tune produce low frequency beat
use a simple peak detector to measure phase at receiver
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two signals with slightly
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observed beats: high carrier
freq, low frequency envelope
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Radio-Interferometric Ranging
Senders (A, B) transmit simultaneously
•pure sinusoid waves at 400 MHz
•small freq difference (<1000 Hz)
Receivers (C, D) measure radio
interference
•sample RSSI (17 kHz)
•find beat frequency, phase offset
•time sync to correlate phase offsets
•result: (dAD-dBD+dBC-dAC) mod λc
dXY: distance between points X and Y
λc: average wave length of carrier freqs
Advantages
ΔφCD /(2fi) = (dAD-dBD+dBC-dAC) mod λc
q-range
•high accuracy (cm)
•long range (200m)
•low cost, low power HW
Previous work
RIPS
inTrack
computes
measures
redundancy
algorithm
in
relative spatial
map of stationary
nodes
Q-ranges
Roles
Heuristics/genetic
SenSys’04,
IPSN’06
location of a
mobile node
(transmitter)
Q-ranges
Refined search
EWSN’07
Analytical formula
(closed form)
MOBISYS’07
Extended Kalman
Filter (EKF)
SenSys’07
Frequencies
Roles
(one infrastructure
node is a transmitter,
rest are receivers),
Frequencies
mTrack
location and
instantaneous
velocity of multiple
mobile nodes
(receivers)
Q-ranges, beat
frequencies
(Doppler shift)
Roles
(two infrastrucrure
nodes transmitters,
rest are receivers)
Frequencies
dTrack
computes location
of a mobile node
(transmitter)
beat frequencies
(Doppler shift)
Roles
(one infrastructure
node is a transmitter,
rest are receivers)
Challenges I.
 RF multipath
 Distorts the phase of the beat signal
 Caused by reflection from objects of similar or
larger size than the wavelength
Challenges I.
 RF multipath
 Distorts the phase of the beat signal
 Caused by reflection from objects of similar or
larger size than the wavelength
Challenges I.
 RF multipath
 Distorts the phase of the beat signal
 Caused by reflection from objects of similar or
larger size than the wavelength
Challenges II.
 Localization algorithms
 Computationally expensive
 Highly redundant measurements
 Measurement noise (due to multipath)
 Requires PC-class hardware
 Measurements are time consuming
1. Time synchronization
2. Calibration: tuning the transmitters to desired frequencies
3. Sampling the RSSI
4. Repeat steps 2 and 3 at multiple center frequencies
5. Report results (multihop routing)
 Tradeoff
 low computational power vs. measurement duration
Approaches I.
 Low carrier frequency
 Decrease multipath indoors
2.4GHz
–
0.125m
433MHz
–
0.69m
3MHz
–
100m
 No modulo arithmetic needed if
wawelength
>
transmission range
 Sufficient to measure phase at a single frequency
 BUT:
 Same velocity results in less Doppler shift
 Antenna size increases
 Limited unlicensed frequency bands at low frequencies
 Redundant carrier frequencies
 Find a consistent set in noisy measurements
Approaches II.
 Redundant architecture nodes
 Use spatial redundancy to mitigate measurement noise
 Many possible measurement configurations possible
 Allows for filtering out inconsistent q-ranges
 Combine RSSI and RI measurements
 Asymmetric architecture
 Shift computation from tags to architecture
 Inexpensive active tags transmitting pure sinusoids
 computationally powerful architecture nodes
 Possibilities
 Increase beat frequency to shorten measurement time
(requires higher sampling frequency at receiver)
 Use multiple sinusoids simultaneously
 Eliminate calibration of beat frequency
Work in progress
 Test platform: asymmetric architecture
 Software defined radio (USRP/GNURadio)
 Berkeley mica2 motes
 Time Synchronization
 SDR transmitter encodes a marking in its signal
 SDR receivers use matched filter to find the position of the marking
 Marking is a Hamming-windowed linear frequency modulated
(chirp) signal
Work in progress
 Test platform: asymmetric architecture
 Software defined radio (USRP/GNURadio)
 Berkeley mica2 motes
 Time Synchronization
 SDR transmitter encodes a marking in its signal
 SDR receivers use matched filter to find the position of the marking
 Marking is a Hamming-windowed linear frequency modulated
(chirp) signal
Work in progress
 Test platform: asymmetric architecture
 Software defined radio (USRP/GNURadio)
 Berkeley mica2 motes
 Time Synchronization
 SDR transmitter encodes a marking in its signal
 SDR receivers use matched filter to find the position of the marking
 Marking is a Hamming-windowed linear frequency modulated
(chirp) signal
 Measurement results
 Average jitter: 1 μs
 Center freq:
 Maximum jitter: 2 μs
 Beat freq:
 Phase:
1 degree
433MHz
1kHz
Questions
?
http://www.isis.vanderbilt.edu/projects/rips/