1. Location awareness and localization Michael Allen 307CRallenm@coventry.ac.uk Much of this lecture is based on a 213 guest lecture on localization given at UCLA by Lewis Girod

2. Location awareness/localization? Where am I relative to known positions? Where am I relative to known positions? Why would I want to know that? Why would I want to know that? Where is this unknown thing relative to me? Where is this unknown thing relative to me? Why do I want to know? Why do I want to know?

3. What are relevant applications? Navigation, tracking Navigation, tracking SatNav, Radar SatNav, Radar Target localization, monitoring Target localization, monitoring Birds, people Birds, people Service awareness Service awareness Smart offices, service discovery Smart offices, service discovery Must be taken in context of application Must be taken in context of application May be (x,y,z) coordinates (or lon, lat) May be (x,y,z) coordinates (or lon, lat) ‘in this room’, ‘near this device’ ‘in this room’, ‘near this device’ Can achieve this actively or passively Can achieve this actively or passively

4. Active Mechanisms Non-cooperative Non-cooperative System emits signal, deduces target location from distortions in signal returns System emits signal, deduces target location from distortions in signal returns e.g. radar and reflective sonar systems e.g. radar and reflective sonar systems Cooperative Target Cooperative Target Target emits a signal with known characteristics; system deduces location by detecting signal Target emits a signal with known characteristics; system deduces location by detecting signal e.g. Active Bat e.g. Active Bat Cooperative Infrastructure Cooperative Infrastructure Elements of infrastructure emit signals; target deduces location from detection of signals Elements of infrastructure emit signals; target deduces location from detection of signals e.g. GPS, MIT Cricket e.g. GPS, MIT Cricket

5. Passive Mechanisms Passive Target Localization Passive Target Localization Signals normally emitted by the target are detected (e.g. birdcall) Signals normally emitted by the target are detected (e.g. birdcall) Several nodes detect candidate events and cooperate to localize it by cross-correlation Several nodes detect candidate events and cooperate to localize it by cross-correlation Passive Self-Localization Passive Self-Localization A single node estimates distance to a set of beacons (e.g. 802.11 bases in RADAR) A single node estimates distance to a set of beacons (e.g. 802.11 bases in RADAR) Blind Localization Blind Localization Passive localization without a priori knowledge of target characteristics Passive localization without a priori knowledge of target characteristics Acoustic “blind beamforming” (Yao et al.) Acoustic “blind beamforming” (Yao et al.) ?

6. Measuring success Simplest way is distance from ‘ground truth’ Simplest way is distance from ‘ground truth’ Euclidean distance from (x,y) estimate to (x,y) truth Euclidean distance from (x,y) estimate to (x,y) truth Other factors Other factors Precision v Accuracy Precision v Accuracy How accurate does it need to be? How accurate does it need to be? Scale Scale Application requirements Application requirements High accuracy, Low precision Low accuracy, High precision

7. Measuring success II The less control we have over the signals we use to estimate position, the less accuracy we can get The less control we have over the signals we use to estimate position, the less accuracy we can get Localizing a bird call is more difficult than acoustic ToF between two nodes Localizing a bird call is more difficult than acoustic ToF between two nodes No synchronisation between un-cooperative targets No synchronisation between un-cooperative targets Even if we control the signals, they may have varying degrees of accuracy Even if we control the signals, they may have varying degrees of accuracy Signal strength vs acoustic/ultrasonic ranging Signal strength vs acoustic/ultrasonic ranging Environmental problems Environmental problems Trade-off between cost, application requirements and environment Trade-off between cost, application requirements and environment

8. Ranging mechanisms Need some way to determine relative distances between unknown and known positions Need some way to determine relative distances between unknown and known positions Timing the reception of signals that are known to propagate at a certain speed are valuable Timing the reception of signals that are known to propagate at a certain speed are valuable Audible acoustic Audible acoustic Ultrasound Ultrasound Radio Radio Other methods based on inverse relationship between loss and distance Other methods based on inverse relationship between loss and distance Received signal strength (RSSI) Received signal strength (RSSI)

9. Time-of-Flight (ToF) Send two signals that propagate at different speeds at the same time Send two signals that propagate at different speeds at the same time Measure the difference in their arrival time and use this to estimate distance Measure the difference in their arrival time and use this to estimate distance Know propagation speeds a priori Know propagation speeds a priori Need to be able to detect FIRST onset of signal Need to be able to detect FIRST onset of signal Problems Problems Non-line of sight, reverb/echoes (multi-path) Non-line of sight, reverb/echoes (multi-path) RF and acoustics are two common examples RF and acoustics are two common examples Radio and ultrasound Radio and ultrasound Radio and audible acoustic Radio and audible acoustic

10. Time-of-Flight (ToF) Example Radio channel is used to synchronize the sender and receiver Radio channel is used to synchronize the sender and receiver Coded acoustic signal is emitted at the sender and detected at the emitter. ToF determined by comparing arrival of RF and acoustic signals Coded acoustic signal is emitted at the sender and detected at the emitter. ToF determined by comparing arrival of RF and acoustic signals CPU Speaker Radio CPU Microphone Radio

11. Multipath/Non line of sight Multipath – when signal bounces off obstacles in the environment Multipath – when signal bounces off obstacles in the environment Causes signal degradation for direct path component Causes signal degradation for direct path component May estimate echoes as actual start of signal = BAD May estimate echoes as actual start of signal = BAD Non line of sight – when there is no direct path between A and B Non line of sight – when there is no direct path between A and B Distance A-B is now biased by some unknown constant – making it an over-estimate Distance A-B is now biased by some unknown constant – making it an over-estimate AB

12. Echoes

13. Ultrasonic and Acoustic ToF Ultrasound better suited to indoor environments and shorter distances (~10m) Ultrasound better suited to indoor environments and shorter distances (~10m) Highly accurate, but highly directional Highly accurate, but highly directional Ultrasound less invasive Ultrasound less invasive Consider application constraints..? Consider application constraints..? Both have multi-path and non-line of sight problems Both have multi-path and non-line of sight problems Echoes cause false/late detections (bias result) Echoes cause false/late detections (bias result) If no direction LoS, cannot ever estimate correct range (not aware that range is incorrect!) If no direction LoS, cannot ever estimate correct range (not aware that range is incorrect!)

14. RSSI/Received Signal Strength RSSI can be used for distance estimation RSSI can be used for distance estimation Loss is inversely proportional to distance covered Loss is inversely proportional to distance covered RSSI is bad for high accuracy RSSI is bad for high accuracy Path loss characteristics depend on environment (1/r n ) Path loss characteristics depend on environment (1/r n ) Shadowing depends on environment Shadowing depends on environment Potential applications Potential applications Approximate localization of mobile nodes, proximity determination Approximate localization of mobile nodes, proximity determination “Database” techniques (RADAR) “Database” techniques (RADAR) Distance RSSI Path loss Shadowing Fading

15. Localization primitives and examples

16. Localization example - GPS Satellites orbit the planet, transmitting coded signals Satellites orbit the planet, transmitting coded signals Atomic clocks, highly accurate Atomic clocks, highly accurate Know own position to high accuracy Know own position to high accuracy Estimate distance through locking into coded sequence from satellite Estimate distance through locking into coded sequence from satellite Our GPS devices have inaccurate clocks Our GPS devices have inaccurate clocks ‘lock onto’ GPS signals from separate satellites ‘lock onto’ GPS signals from separate satellites Create local versions of the signals they are sending Create local versions of the signals they are sending Figure out offset of our version to theirs = ToF Figure out offset of our version to theirs = ToF 3 ranges to satellites minimum req’d 3 ranges to satellites minimum req’d Solve problem using tri-lateration Solve problem using tri-lateration Accuracy of metres Accuracy of metres

17. Tri-lateration/multi-lateration Given several ‘known’ positions, and distances from these to an unknown source, we can estimate the position of the unknown Given several ‘known’ positions, and distances from these to an unknown source, we can estimate the position of the unknown In 2D this is figuring out the intersection of circles, in 3D is intersection of spheres (slightly harder) In 2D this is figuring out the intersection of circles, in 3D is intersection of spheres (slightly harder) 3 minimum to resolve 2D ambiguity, 4 for 3D 3 minimum to resolve 2D ambiguity, 4 for 3D BUT - GPS can get away with 3 – how come? BUT - GPS can get away with 3 – how come? Important ‘primitive’ in position estimation Important ‘primitive’ in position estimation WSN Localization algorithms often built on top of this WSN Localization algorithms often built on top of this Multi-lateration is when you use more than 3 Multi-lateration is when you use more than 3 The generalisation for many observations and 3D The generalisation for many observations and 3D

18. Geometry matters!!! If known positions are bunched together and the unknown is far away from them Geometric Dilution of Precision can occur If known positions are bunched together and the unknown is far away from them Geometric Dilution of Precision can occur The angles relative to the unknown are too similar, and the precision of the position estimate is compromised The angles relative to the unknown are too similar, and the precision of the position estimate is compromised Estimate can get ‘pushed’ out with poor distance estimation Estimate can get ‘pushed’ out with poor distance estimation Best geometry is the ‘convex hull’ (unknown is surrounded) Best geometry is the ‘convex hull’ (unknown is surrounded) GOODBAD

19. Active bats/active badge AT&T Cambridge (as was) AT&T Cambridge (as was) Location system Location system Badge – infrared, room granularity Badge – infrared, room granularity Bats – ultrasonic, 3D position within room Bats – ultrasonic, 3D position within room Uses ultrasonic ranging Uses ultrasonic ranging Devices broadcast unique ‘pings’ Devices broadcast unique ‘pings’ Trilateration/multilateration Trilateration/multilateration Can use same ‘cheat’ as GPS Can use same ‘cheat’ as GPS Ceiling mounted detectors Ceiling mounted detectors Centralised computation Centralised computation Device doesn’t know where it is, system does Device doesn’t know where it is, system does Badge Bat

20. Cricket location support system Similar application ideas to active bats Similar application ideas to active bats Part of MIT oxygen project Part of MIT oxygen project Active beacons and passive listeners Active beacons and passive listeners Beacons broadcast, devices can figure out where they are Beacons broadcast, devices can figure out where they are Scales well Scales well Decentralised Decentralised Low-power, reconfigurable Low-power, reconfigurable

21. Radar/Microsoft Uses signal strength (RSSI) to collect signature traces of users (with laptops – 802.11) Uses signal strength (RSSI) to collect signature traces of users (with laptops – 802.11) These traces can be matched to known RSSI signatures held in a database These traces can be matched to known RSSI signatures held in a database Position can be estimated based on comparison Position can be estimated based on comparison Median accuracy 2-3 metres, large variance Median accuracy 2-3 metres, large variance Problems – RSSI is not accurate, estimates will vary even when stationary! Problems – RSSI is not accurate, estimates will vary even when stationary! Expect best of ~1 – 1.5m accuracy Expect best of ~1 – 1.5m accuracy Is this good enough? Is this good enough? Motetrack* at Harvard did similar with motes Motetrack* at Harvard did similar with motes *http://www.eecs.harvard.edu/~konrad/projects/motetrack/

22. Localization in a wireless sensor networking context We deploy a wireless sensor network because we want to sense and process data related to a physical phenomena We deploy a wireless sensor network because we want to sense and process data related to a physical phenomena Need to determine physical locations of sensors to put context to data being gathered Need to determine physical locations of sensors to put context to data being gathered Granularity relates to application, scale Granularity relates to application, scale

23. Goals of WSN localization Minimise the amount of known locations we need a priori Minimise the amount of known locations we need a priori Can’t just give all nodes GPS.. Can we? Can’t just give all nodes GPS.. Can we? Estimate ranges as cheaply as possible Estimate ranges as cheaply as possible Use hardware we already have/need to use Use hardware we already have/need to use Maximise accuracy Maximise accuracy Relative to our application Relative to our application Consider scale, granularity Consider scale, granularity

24. Multi-hop localization In previous examples, devices have always been 1 logical hop away from known positions In previous examples, devices have always been 1 logical hop away from known positions Not necessarily the case in wireless sensor networks Not necessarily the case in wireless sensor networks Need to design algorithms to deal with this problem Need to design algorithms to deal with this problem Consider error in measurement propagates over multiple hops Consider error in measurement propagates over multiple hops Especially bad in large networks, with poor ranging techniques Especially bad in large networks, with poor ranging techniques

25. Case study: Acoustic ENSBox Designed for acoustic sensing applications Designed for acoustic sensing applications Example: localizing animals based on their calls Example: localizing animals based on their calls Passive, non-cooperative Passive, non-cooperative Highly accurate self-localization Highly accurate self-localization Acoustic ToF ranging and DoA Acoustic ToF ranging and DoA Iterative multi-lateration algorithm Iterative multi-lateration algorithm Requires no a priori information Requires no a priori information Accuracy is important for application Accuracy is important for application Using self-localization as ground-truth for localizing animals Using self-localization as ground-truth for localizing animals Nodes have 48KHz sampling, powerful processors, large amount of memory Nodes have 48KHz sampling, powerful processors, large amount of memory V2 (2007)

26. Source-localization Processing chain: Processing chain: Detect event (we don’t control signal) Detect event (we don’t control signal) Estimate DoA (Problem: cannot rely on ToF) Estimate DoA (Problem: cannot rely on ToF) Group similar events together Group similar events together Fuse data Fuse data One node = sub array All nodes = array

27. Results Ground truth is hard to define when you’re estimating non-cooperative sources! Ground truth is hard to define when you’re estimating non-cooperative sources! Best hope is precision Best hope is precision

28. Conclusions Location awareness/localization is important Location awareness/localization is important Considered in context!! Considered in context!! High accuracy can be achieved, dependent on ranging technology, constraints of environment High accuracy can be achieved, dependent on ranging technology, constraints of environment Need to consider application requirements Need to consider application requirements There are many different ranging approaches There are many different ranging approaches Approaches vary based on indoor/outdoor, size of devices, cost, goals Approaches vary based on indoor/outdoor, size of devices, cost, goals Multi-hop ranging brings other challenges Multi-hop ranging brings other challenges Propagation of error.. Propagation of error..