Location awareness and localization

1. Location awareness and localization Michael Allen 307CR allenm@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? • Why would I want to know that? • Where is this unknown thing relative to me? • Why do I want to know?

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

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

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

6. Measuring success • Simplest way is distance from ‘ground truth’ • Euclidean distance from (x,y) estimate to (x,y) truth • Other factors • Precision v Accuracy • How accurate does it needto be? • Scale • 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 • Localizing a bird call is more difficult than acoustic ToF between two nodes • No synchronisation between un-cooperative targets • Even if we control the signals, they may have varying degrees of accuracy • Signal strength vs acoustic/ultrasonic ranging • Environmental problems • Trade-off between cost, application requirements and environment

8. Ranging mechanisms • 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 • Audible acoustic • Ultrasound • Radio • Other methods based on inverse relationship between loss and distance • Received signal strength (RSSI)

9. Time-of-Flight (ToF) • 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 • Know propagation speeds a priori • Need to be able to detect FIRST onset of signal • Problems • Non-line of sight, reverb/echoes (multi-path) • RF and acoustics are two common examples • Radio and ultrasound • Radio and audible acoustic

10. Time-of-Flight (ToF) Example • 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 Radio Radio CPU CPU Speaker Microphone

11. Multipath/Non line of sight • Multipath – when signal bounces off obstacles in the environment • Causes signal degradation for direct path component • May estimate echoes as actual start of signal = BAD • 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 A B

12. Echoes

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

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

15. Localization primitives and examples

16. Localization example - GPS • Satellites orbit the planet, transmitting coded signals • Atomic clocks, highly accurate • Know own position to high accuracy • Estimate distance through locking into coded sequence from satellite • Our GPS devices have inaccurate clocks • ‘lock onto’ GPS signals from separate satellites • Create local versions of the signals they are sending • Figure out offset of our version to theirs = ToF • 3 ranges to satellites minimum req’d • Solve problem using tri-lateration • 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 • 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 • BUT - GPS can get away with 3 – how come? • Important ‘primitive’ inposition estimation • WSN Localization algorithmsoften built on top of this • Multi-lateration is when you usemore than 3 • The generalisation for many observationsand 3D

18. Geometry matters!!! • If known positions are bunched together and the unknown is far away from themGeometric Dilution of Precision can occur • 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 • Best geometry is the ‘convex hull’ (unknown is surrounded) GOOD BAD

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

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

21. Radar/Microsoft • 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 • Position can be estimated based on comparison • Median accuracy 2-3 metres, large variance • Problems – RSSI is not accurate, estimates will vary even when stationary! • Expect best of ~1 – 1.5m accuracy • Is this good enough? • 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 • Need to determine physical locations of sensors to put context to data being gathered • Granularity relates to application, scale

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

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

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

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

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

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