213 localization

8/2/2019 213 Localization

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LOCALIZATION

Distributed Embedded Systems

CS 213/Estrin/Winter 2002Speaker: Lewis Girod


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What is Localization

A mechanism for discoveringspatial relationships betweenobjects


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Why is Localization Important?

Large scale embedded systems introduce manyfascinating and difficult problems

This makes them much more difficult to use

BUT it couples them to the physical world Localization measures that coupling, giving raw

sensor readings a physical context

Temperature readings temperature map

Asset tagging asset tracking Smart spaces context dependent behavior

Sensor time series coherent beamforming


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Variety of Applications

Two applications:

Passive habitat monitoring:Where is the bird?

What kind of bird is it?

Asset tracking:Where is the projector?

Why is it leaving the room?


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Variety of Application Requirements

Outdoor operation

Weather problems

Bird is not tagged

Birdcall is characteristicbut not exactly known

Accurate enough tophotograph bird

Infrastructure:

Several acoustic sensors,with known relativelocations; coordinationwith imaging systems

Indoor operation

Multipath problems

Projector is tagged

Signals from projector tagcan be engineered

Accurate enough to trackthrough building

Infrastructure:

Room-granularity tagidentification andlocalization; coordinationwith security infrastructure

Very different requirements!


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Multidimensional Requirement Space

Granularity & Scale

Accuracy & Precision

Relative vs. Absolute Positioning

Dynamic vs. Static (Mobile vs. Fixed) Cost & Form Factor

Infrastructure & Installation Cost

Communications Requirements Environmental Sensitivity

Cooperative or Passive Target


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Axes of Application Requirements

Granularity and scale of measurements:

What is the smallest and largest measurable distance?

e.g. cm/50m (acoustics) vs. m/25000km (GPS)

Accuracy and precision: How close is the answer to ground truth (accuracy)?

How consistent are the answers (precision)?

Relation to established coordinate system:

GPS? Campus map? Building map?

Dynamics:

Refresh rate? Motion estimation?


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Axes of Application Requirements

Cost: Node cost: Power? $? Time?

Infrastructure cost? Installation cost?

Form factor: Baseline of sensor array

Communications Requirements: Network topology: cluster head vs. local determination

What kind of coordination among nodes?

Environment: Indoor? Outdoor? On Mars?

Is the target known? Is it cooperating?


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Variety of Mechanisms

Weve seen a broad spectrum of application

requirements

There are also a broad spectrum of

localization mechanisms appropriate fordifferent applications


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Returning to our two Applications

Choice of mechanisms differs:

Passive habitat monitoring:Minimize environ. interference

No two birds are alike

Asset tracking:Controlled environment

We know exactly what tag is like


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Variety of Localization Mechanisms

Bird is not tagged

Passive detection of birdpresence

Birdcall is characteristic

but not exactly known

Bird does not have radio;TDOA measurement

Passive target localization

Requires Sophisticated detection

Coherent beamforming

Large data transfers

Projector is tagged

Projector mightknow it had moved

Signals from projector tag

can be engineered

Tag can use radio signal toenable TOF measurement

Cooperative Localization

Requires Basic correlator

Simple triangulation

Minimal data transfers

Very different mechanisms indicated!


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Taxonomy of Localization Mechanisms

Active Localization System sends signals to localize target

Cooperative Localization The target cooperates with the system

Passive Localization System deduces location from observation of

signals that are already present

Blind Localization System deduces location of target without a priori

knowledge of its characteristics


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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. ORL Active Bat, GALORE Panel, AHLoS

Cooperative Infrastructure Elements of infrastructure emit signals; target

deduces location from detection of signals

e.g. GPS, MIT Cricket

TargetSynchronization channelRanging channel


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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 andcooperate 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 [Bahlet al.], Ricochet in Bulusu et al.)

Blind Localization Passive localization without a priori

knowledge of target characteristics

Acoustic blind beamforming (Yao et al.)

?

TargetSynchronization channelRanging channel


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Active vs. Passive

Active techniques tend to work best Signal is well characterized, can be engineered for noise

and interference rejection

Cooperative systems can synchronize with the target toenable accurate time-of-flight estimation

Passive techniques Detection quality depends on characterization of signal

Time difference of arrivals only; must surround target withsensors or sensor clusters

TDOA requires precise knowledge of sensor positions Blind techniques

Cross-correlation only; may increase communication cost

Tends to detect loudest event.. May not be noise immune


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Building Localization Systems

Given a set of application requirements, howdo we build a system that meets them?

Outline:

Overview of a typical system design

A quick example

Ranging technologies

Coordinate system synthesis techniques Spatial scalability

Recent results: the GALORE panel


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Localization System Components

Generally speaking, what is involved with alocalization system?

Coordinate SystemSynthesis

ParameterEstimation

Filtering

ParameterEstimation

Filtering

ParameterEstimation

Filtering

Parameters might include:

Range between nodesAngle between nodesPsuedorange to target (TDOA)Bearing to target (TDOA)Absolute orientation of nodeAbsolute location of node (GPS)


ParameterEstimation

Filtering

ParameterEstimation

Filtering

ParameterEstimation

Filtering

Filtering

Stitching/Merging This step applies to distributedconstruction of large-scalecoordinate systems

This step estimates targetcoordinates (and often otherparameters simultaneously)


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Example of a Localization System

Unattended Ground Sensor and acousticlocalization system, developed at Sensoria Corp.

12 cm

Microphone

Speaker

Each node has 4 speaker/microphone pairs, arrangedalong the circumference of theenclosure. The node also has aradio system and an orientation

sensor.


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System Architecture

Ranging between nodes based on detection of coded acousticsignals, with radio synchronization to measure time of flight

Angle of arrival is determined through TDOA and is used to estimatebearing, referenced from the absolute orientation sensor

An onboard temperature sensor is used to compensate for the effectof environmental conditions on the speed of sound


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System Architecture

Pairwise ranges and angles are transmitted to a cluster-head, wherea multilateration algorithm computes a consistent coordinate system

Cluster heads exchange their coordinate systems, which are thenstitched together into larger coordinate systems

Range, Angular DataRange, Angular DataRange, Angular Data Multilat Engine

Range, Angular DataRange, Angular Data

Range, Angular Data

Multilat Engine

Merge Engine

Merge Engine


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Sensing layer: Ranging, AOA, etc.


ParameterEstimation

Filtering

ParameterEstimation

Filtering

ParameterEstimation

Filtering




ParameterEstimation

Filtering

ParameterEstimation

Filtering

ParameterEstimation

Filtering

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ParameterEstimation

Filtering

ParameterEstimation

Filtering




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Active and Cooperative Ranging

Measurement of distance between two points Acoustic

Point-to-point time-of-flight, using RF synchronization

Narrowband (typ. ultrasound) vs. Wideband (typ. audible)

RF RSSI from multiple beacons Transponder tags (rebroadcast on second frequency),

measure round-trip time-of-flight.

UWB ranging (averages many round trips)

Psuedoranges from phase offsets (GPS)

TDOA to find bearing, triangulation from multiple stations Visible light

Stereo vision algorithms Need not be cooperative, but cooperation simplifies the problem


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Passive and Non-cooperative Ranging

Generally less accurate than active/cooperative Acoustic

Reflective time-of-flight (SONAR)

Coherent beamforming (Yao et al.)

RF

Reflective time-of-flight (RADAR systems) Database techniques

RADAR (Bahl et al.) looks up RSSI values in database

RadioCamera is a technique used in cellular infrastructure;measures multipath signature observed at a base station

Visible light Laser ranging systems

Commonly used in robotics; very accurate

Main disadvantage is directionality, no positive ID of target


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Using RF for Ranging

Disadvantages of RF techniques Measuring TOF requires fast clocks to achieve high

precision (c 1 ft/ns)

Building accurate, deterministic transponders is verydifficult Temperature-dependence problems in timing of path from

receiver to transmitter

Systems based on relative phase offsets (e.g. GPS) requirevery tight synchronization between transmitters

Ultrawide-band ranging for sensor nets? Current research focus in RF community

Based on very short wideband pulses, measure RTT

May encounter licensing problems


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Practical Difficulties with RSSI RSSI is extremely problematic for

fine-grained, ad-hoc applications Path loss characteristics depend on

environment (1/rn)

Shadowing depends on environment

Short-scale fading due to multipathadds random high frequency

component with huge amplitude (30-60dB) very bad indoors Mobile nodes might average out

fading.. But static nodes can be stuckin a deep fade forever

Possible applications

Approximate localization of mobilenodes, proximity determination

Database techniques (RADAR)

More sophisticated radios?

Distance

RSSI

Path lossShadowingFading

Ref. Rappaport, T, Wireless CommunicationsPrinciple and Practice, Prentice Hall, 1996.


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Using Acoustics for Ranging

Key observation: Sound travels slowly! Tight synchronization can easily be achieved using RF

signaling

Slow clocks are sufficient (v= 1 ft/ms)

With LOS, high accuracy can be achieved cheaply

Coherent beamforming can be achieved with low samplerates

Disadvantages Acoustic emitters are power-hungry (must move air)

Obstructions block sound completely detector picks upreflections

Existing ultrasound transducers are narrowband


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Typical Time-of-Flight AR System

Radio channel is used to synchronize the senderand receiver (or use a service like RBS!)

Coded acoustic signal is emitted at the sender anddetected at the emitter. TOF determined by

comparing arrival of RF and acoustic signals

CPU

Speaker

Radio

CPU

Microphone

Radio


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Narrowband vs. Wideband

Narrowband technique: pulse train at f0 Works with tuned resonant ultrasound transducers

COTS parts implement detection (SONAR modules)

Crosstalk between nodes is a problem, introducessignificant coordination overhead to system design

Used in ORL Active Bat, MIT Cricket, UCLA AHLoS

Wideband technique: pseudonoise burst Detection requires ~100M FLOPs, ~128K RAM

High accuracy, excellent interference rejection

30m range easily achieved over grass in outdoor environ. Excellent crosstalk rejection; each xmitter uses diff. code

Used in GALORE Panel, Sensoria Ground Sensor


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Wideband Acoustic Detection

Autocorrelation

First arrivalandechoes

Offset in samples (20.8s, or 0.71 cm)

ValueofCorrelationFunction


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Ringing

Ringing Introduced by Speaker Probable Echo

Can be corrected at the source (deconvolve source waveform) or at the receiver (correlation)


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An Acoustic Ranging Error Model

A useful model for error in acoustic ranges isRij= ||XiXj||2 + nij+ Nij,

where

nij is a gaussian error term (=0,=1.3)

Nij

is a fixed bias present only when LOS blocked

Error reduction: nijcan be reduced by repeated observations

Nijcannot because it is caused by persistent features ofthe environment, such as detection of a reflection.

The Nijerrors must be filtered at higher layers Cross-validation of multiple sensor modalities

Geometric consistency, error terms during multilateration


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Typical Angle-of-Arrival AR System

TOF AR system with multiple receiver channels

Time difference of arrivals at receiver used toestimate angle of arrival

CPU

Speaker

Radio

CPU

Radio

MicrophoneMicrophoneMicrophoneMicrophone

Array


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Multi-channel Correlation

Arrival times at the four channels

Offset in samples (20.8s, or 0.71 cm)

ValueofCorrelationFunction


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Sources of error in bearing & position est.

Quantization error in detection Sample rate of detector lower bounds phase accuracy

Synchronization error

Detection error

Noise, interference, ringing, blurring in channel

Excess path length due to clutter

Non-LOS detection of reflected paths

Usually easy to filter if sensor positions are known

Angular dependence in sensor

Error in measurement or estimation of baseline


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Parallel Rays Approximation

Parallel rays approximation True for distant point source

= acos(/B)

At shallow angles, smallvariations in relative range

yield large angular errors When is it valid?

Red is error in degreesbetween parallel rays andtrue value

Blue region shows caseswhere error is less than 0.5degrees

B


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Intuition from parallel rays

Error in bearing estimate from quantization error

Quantization error is error caused by sample timing

Derivative of acos(x) represents sensitivity to small errorsin relative ranges

B

X= ()/BX1 as 0


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Coordinate system synthesis layer


ParameterEstimation

Filtering

ParameterEstimation

Filtering

ParameterEstimation

Filtering




ParameterEstimation

Filtering

ParameterEstimation

Filtering

ParameterEstimation

Filtering

Filtering


ParameterEstimation

Filtering

ParameterEstimation

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Position Est. and Coord. Systems

Position Estimation, Triangulation Some of the nodes have known positions

Targets position inferred relative to known nodes

e.g. Active Bat, single GALORE Panel Forming a coordinate system, Multilateration

Most nodes have unknown positions

Consistent coordinate system constructed based

on measured relationships between nodes Multilateration is a commonly used term

e.g. AHLoS, multi-panel GALORE system


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Optimization Problems

Often implemented as an overconstrainedoptimization problem:

Input is set of measurements

Ranges, angles, other relationships Output is estimated node position map

Environmental parameters often estimated concurrently

Gaussian error least-squares minimization

Careful filtering required to ensure this property


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Simple Example: GALORE Panel Pythagorean Theorem:

Object is to find position estimate that minimizessquared sum of error terms

iii zyx ,,

zyx ,,

ir

2222 iiiii rzzyyxx

Where is measurement error in the range measurementi

i

r


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GALORE Panel Position Estimator

Rewrite to get error as function of position:

Problem: error function is not linear Approximate the error function by a Taylors series (where X is

position vector):

Neglecting higher-order terms, and choosing an initial guess X0,we have a linear approximation of the error function in that

neighborhood Iteratively improve X until sufficient convergence

Good results if problem is overconstrained

...)(")(')(|)( 00

XfXfXfXfX

iiiii rzzyyxx 222

Ref. Strang, and G, Borre, K, Linear Algebra, Geodesy,and GPS, Wellesley-Cambridge Press, 1997


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AHLoS Iterative Multilateration

Unlike case of single GALORE Panel

Relative positions of sensors not known a priori

An iterative approach is taken, where each step solves theposition of one or two more nodes

Atomic Multilateration

One or two unknown nodes and several known nodes;similar in approach to the previous slides

Collaborative Multilateration

Several unknown and known nodes

Set of non-linear equations based on pythagorean theorem

Solved using gradient descent or simulated annealing

Ref. A. Savvides, C Han, M. Srivastava, Dynamic Fine-GrainedLocalization in Ad-Hoc Netoworks of Sensors, Mobicom 2001.


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Stitching and Network Coordinate Transforms


ParameterEstimation

Filtering

ParameterEstimation

Filtering

ParameterEstimation

Filtering




ParameterEstimation

Filtering

ParameterEstimation

Filtering

ParameterEstimation

Filtering

Filtering


ParameterEstimation

Filtering

ParameterEstimation

Filtering






Filtering



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Spatially Scalable Coordinate Systems

Consider an infinite fieldof sensor nodes Global optimization of entire

field is not scalable

Locally optimized

patches Simple stitching operation:

find transformation that bestmatches common nodes

2nd order optimization:optimize overlap regions

Tie systems down to surveypoints to combat cumulativeerror


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Network Coordinate Transforms

Idea from RBS: transform to local time at every hop

Improves scalability by avoiding need for global time

Similar technique may be useful for localization

Transform to local coordinate system at each hop However,

Error propagation characteristics not well understood: willcumulative error result in excessive drift?

Depends a great deal on achieving an upper bound on per-

hop error; feasibility of this is not yet understood


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Recent Results: GALORE Panel

GALORE Panel Localization System

The GALORE panel is designed to provide localizationservices for a field of small systems called motes

Computational cost of sender is low; Panel does detection

61 cm

MicrophonesSpeakers

Each panel has 4 speaker/microphone pairs, placed in thecorners of the panel. The panelalso has a radio system that isused to synchronize with other

panels and with the mote field.

Acoustic Mote adds spkr, amp

on daughterboard (N. Busek)


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Current Status

Blue rectangleincicates position ofpanel

Red points areactual positions ofmotes

Green points arepositions estimatedby the panel

Five trials weretaken at each

position

Source: NEST PI Slides,Feb 2002


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Next Steps

Formation of inter-panelcoordinate system Inter-panel ranging to

accurately estimate relativeposition and orientation

Multilateration techniques tooptimize away error amongmany panels

RBS + interpanel coordinatetransforms will enable

coherent processing of datafrom multiple panels

Problem: Non-LOS paths,filtering of range data

61cm

Inter-panel coordination

Mote-panel coordination

Mote-mote coordination


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Next Steps: Applications

Tracking in a mote field

Acoustic threshold detection in mote field triggersresponses (N. Busek)

Using RBS and the GALORE Localization system, motes will

be able to correlate their observations in time and space

Coherent Signal Processing

One or more panels can collaborate to do passivelocalization and beamforming (H. Wang)

RBS provides accurate synchronization GALORE Localization system determines precise relative

positions of the receivers.

213 localization

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