Location awareness and localization

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Location awareness and localization

• Michael Allen
• 307CR
• allenm_at_coventry.ac.uk

Much of this lecture is based on a 213 guest
lecture on localization given at UCLA by Lewis
Girod
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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

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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.)

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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
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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

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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)

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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

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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
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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
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Echoes
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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!)

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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
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Localization primitives and examples
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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 reqd
• Solve problem using tri-lateration
• Accuracy of metres

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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

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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
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Active bats/active badge

• ATT 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 doesnt know where it is, system does

Bat
Badge
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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

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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/mote
track/
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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

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Goals of WSN localization

• Minimise the amount of known locations we need a
  priori
• Cant 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

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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

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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

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Source-localization

• Processing chain
• Detect event (we dont control signal)
• Estimate DoA (Problem cannot rely on ToF)
• Group similar events together
• Fuse data

One node sub array All nodes array
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Results

• Ground truth is hard to define when youre
  estimating non-cooperative sources!
• Best hope is precision

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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..