Target tracking

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A Survey on Tracking A Survey on Tracking Methods for a Methods for a Wireless Sensor Wireless Sensor Network Network Taylor Flagg, Beau Hollis Taylor Flagg, Beau Hollis & Francisco J. Garcia- & Francisco J. Garcia- Ascanio Ascanio

description

Target tracking in a wireless sensor networks

Transcript of Target tracking

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A Survey on Tracking A Survey on Tracking Methods for a Wireless Methods for a Wireless

Sensor NetworkSensor NetworkTaylor Flagg, Beau Hollis & Taylor Flagg, Beau Hollis &

Francisco J. Garcia-Ascanio Francisco J. Garcia-Ascanio

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OverviewOverview

Sensor Network TrackingSensor Network Tracking Hierarchical Approach Hierarchical Approach Hidden Markov Model with Binary SensorsHidden Markov Model with Binary Sensors Compare and ContrastCompare and ContrastPursuit Evasion Games Pursuit Evasion Games

Two-Tier Approach Two-Tier Approach Multi-Hop ApproachMulti-Hop Approach Ant-Based ApproachAnt-Based Approach Compare and ContrastCompare and ContrastConclusionConclusion

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Sensor Network TrackingSensor Network Tracking

Tracking an object moving through a field Tracking an object moving through a field of sensorsof sensors Smart HouseSmart House Air Traffic ControlAir Traffic Control Fleet MonitoringFleet Monitoring SecuritySecurity

Many sensor types can be usedMany sensor types can be used

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Hierarchical ApproachHierarchical Approach

STUN: Scalable Tracking Using STUN: Scalable Tracking Using Networked sensorsNetworked sensors Sensor network described as a hierarchical Sensor network described as a hierarchical

graphgraph Each node has a detection setEach node has a detection set Object positions are queried from the root Object positions are queried from the root

using detection setsusing detection sets

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Detection SetsDetection Sets

Nodes broadcast detected objectsNodes broadcast detected objectsParents broadcast set of objects detected Parents broadcast set of objects detected by their child nodesby their child nodesOnly broadcast when set changesOnly broadcast when set changesRedundant massages are prunedRedundant massages are pruned

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Graph weightsGraph weights

The sensor graph is weighted based on The sensor graph is weighted based on movement patternsmovement patternsHigher weight means more objects Higher weight means more objects transition between those two nodestransition between those two nodes

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Communication CostCommunication Cost

Depends on number of messages Depends on number of messages transmittedtransmittedTree structure affect costTree structure affect cost

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DAB – Drain and BalanceDAB – Drain and Balance

IdeaIdea Imagine flooding a mountain rangeImagine flooding a mountain range At each step water level is lowered and visible At each step water level is lowered and visible

peaks are added to the treepeaks are added to the treeActual AlgorithmActual Algorithm Set a weight thresholdSet a weight threshold Add balanced sets of with weights above the Add balanced sets of with weights above the

thresholdthreshold Iteratively lower threshold and reapplyIteratively lower threshold and reapply

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Drain and Balance ExampleDrain and Balance Example

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Using Hidden Markov Model to Track Using Hidden Markov Model to Track with Binary Sensorswith Binary Sensors

Binary sensors only report if an object is Binary sensors only report if an object is detected or notdetected or notReduces affect of calibration and error Reduces affect of calibration and error Sensor location is not neededSensor location is not neededObject paths are based on statistical Object paths are based on statistical analysisanalysis

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GraphGraph

Sensor graph with links for adjacent sensorsSensor graph with links for adjacent sensorsGraph forms Hidden Markov Model (HMM)Graph forms Hidden Markov Model (HMM)HMM is used to calculate probable object pathsHMM is used to calculate probable object pathsPath prediction uses the Path prediction uses the Viterbi Viterbi AlgorithmAlgorithm

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ImplementationImplementation

Each node stores 3 values required for the Each node stores 3 values required for the path calculationpath calculation Probability of an object starting at that nodeProbability of an object starting at that node Probability that objects will be accurate Probability that objects will be accurate

detected (accounts for sensor error)detected (accounts for sensor error) Matrix of probabilities for transition to another Matrix of probabilities for transition to another

node in the node’s neighborhood node in the node’s neighborhood

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Pruning and OverlapPruning and Overlap

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SimilaritiesSimilarities

Avoid localization issues by graphing Avoid localization issues by graphing sensor topologysensor topologyCommunicate in between nodes rather Communicate in between nodes rather than flooding the networkthan flooding the networkPruning redundant informationPruning redundant informationUse pre-computed probabilities and Use pre-computed probabilities and weights to gain efficiencyweights to gain efficiency

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DifferencesDifferences

HMMHMM Operates on binary Operates on binary

sensorssensors Processes all Processes all

necessary necessary information in each information in each individual node, individual node, distributes trackingdistributes tracking

Communicates back Communicates back and forth among and forth among neighbors neighbors

STUNSTUN Made for non-uniform Made for non-uniform

movementmovement Leaves actual Leaves actual

tracking to a tracking to a centralized query-centralized query-pointpoint

Only communicates Only communicates up hierarchy tree up hierarchy tree

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Pursuit Evasion GamesPursuit Evasion Games

Autonomous agents (Pursuers) pursue Autonomous agents (Pursuers) pursue one or more non-cooperative agents one or more non-cooperative agents (evaders)(evaders)Sensor networks are used to detect Sensor networks are used to detect evadersevaders

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Pursuit Evasion GamesPursuit Evasion Games

In traditional PEG’s In traditional PEG’s The evaders attempt to avoid detection and The evaders attempt to avoid detection and

capture by varying speed and directioncapture by varying speed and direction

Different forms of PEG’s consist ofDifferent forms of PEG’s consist of Rescue operationsRescue operations SurveillanceSurveillance Localization and tracking of moving parts in a Localization and tracking of moving parts in a

warehouse, etc.warehouse, etc.

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Lower TierLower Tier Numerous nodesNumerous nodes Handles simple detectionHandles simple detection Limited resourcesLimited resources Provide basic informationProvide basic information Power conservationPower conservation Results gathered don’t need to be perfectResults gathered don’t need to be perfect Leader election algorithm based on strongest Leader election algorithm based on strongest

detectiondetection

Two-Tier Approach Two-Tier Approach

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Two-Tier Approach Two-Tier Approach

Higher TierHigher Tier Fewer nodesFewer nodes Nodes are more complex (e.g. sophisticated Nodes are more complex (e.g. sophisticated

camera nodes.)camera nodes.) Handles processing and initiates actionsHandles processing and initiates actions Resulting actions sent to the pursuerResulting actions sent to the pursuer

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Pursuer has its own onboard software Pursuer has its own onboard software service for interception and navigationservice for interception and navigation Receives detection events from the networkReceives detection events from the network Determines if event was caused by the Determines if event was caused by the

evader, another pursuer, or noiseevader, another pursuer, or noise Pursuer only needs data from the network Pursuer only needs data from the network

every few secondsevery few seconds Uses GPS to calculate an interception Uses GPS to calculate an interception

destinationdestination

Pursuer in Two Tier System Pursuer in Two Tier System

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Multi-Hop ApproachMulti-Hop Approach

Sensor nodes estimate evader positions Sensor nodes estimate evader positions and push their data to other nodes and to and push their data to other nodes and to the pursuerthe pursuerSuper nodes Super nodes Receive data and do processing to get a Receive data and do processing to get a

composite estimatecomposite estimate Collaborate with neighbors to further improve Collaborate with neighbors to further improve

the estimates the estimates Broadcast final estimate to pursuerBroadcast final estimate to pursuer

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Multi-Hop ProblemsMulti-Hop Problems

Cost effective sensors are problematicCost effective sensors are problematic Small power supplySmall power supply Low detection probabilityLow detection probability High false alarm rateHigh false alarm rate

With each hop, likelihood of transmission With each hop, likelihood of transmission failure and packet delays increasefailure and packet delays increase

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Ant-Based ApproachAnt-Based Approach

Based on how ants gather foodBased on how ants gather food Ants leave trail of pheromones Ants leave trail of pheromones Other ants follow the direction in which Other ants follow the direction in which

pheromones are most intensepheromones are most intense

Sensors store a timestamp of evader Sensors store a timestamp of evader detectiondetectionPursuer looks compares timestamps in a Pursuer looks compares timestamps in a region to derive the evaders directionregion to derive the evaders direction

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Ant-Based ImplementationAnt-Based Implementation

Ant-Based approach is broken down into Ant-Based approach is broken down into three phases:three phases: Reporting the Initial PositionReporting the Initial Position Initiation of TrackingInitiation of Tracking TrackingTracking

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Reporting the Initial PositionReporting the Initial Position

Starts when first sensor detects evader. Starts when first sensor detects evader. This node will do the followingThis node will do the following Contacts pursuerContacts pursuer Broadcast to entire network about the evader Broadcast to entire network about the evader

and suppresses other nodes from contacting and suppresses other nodes from contacting the purser with redundant informationthe purser with redundant information

Subsequent nodes will send new information to Subsequent nodes will send new information to the purser but not the entire networkthe purser but not the entire network

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Initiation of TrackingInitiation of Tracking

Pursuer heads toward the first node to Pursuer heads toward the first node to detect the evaderdetect the evaderPursuer queries nearby nodes for Pursuer queries nearby nodes for timestampstimestampsThese timestamps are used to determine These timestamps are used to determine the velocity vectorthe velocity vector

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TrackingTracking

Pursuer intelligently queries only nodes in Pursuer intelligently queries only nodes in the direction of the velocity vectorthe direction of the velocity vectorCompares timestamps and looks for larger Compares timestamps and looks for larger timestamp valuetimestamp valueCuts down on communication costsCuts down on communication costsThe velocity vector is updated and the The velocity vector is updated and the process is repeated until the evader is process is repeated until the evader is captured or leaves the networkcaptured or leaves the network

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SimilaritiesSimilarities

Sensor nodes are pre-established in the Sensor nodes are pre-established in the region that the evader will occupyregion that the evader will occupySystems provide a lower tier of nodes Systems provide a lower tier of nodes that only collect evader datathat only collect evader data

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DifferencesDifferencesTwo-TierTwo-Tier

Higher tier contain processing and tracking Higher tier contain processing and tracking algorithmsalgorithmsDedicated software services located on the Dedicated software services located on the pursuerpursuerElect a leader node to distribute Elect a leader node to distribute informationinformationResults don’t need to be perfectResults don’t need to be perfectLeader election based on strongest Leader election based on strongest detectiondetection

Multi-HopMulti-HopHigher tier nodes contain Higher tier nodes contain processing and tracking algorithmsprocessing and tracking algorithmsCollaborates with neighboring super Collaborates with neighboring super nodes to improve estimatesnodes to improve estimatesSuper node similar to leader Super node similar to leader election to propagate information to election to propagate information to pursuerpursuer

Ant-BasedAnt-BasedNodes collect timestamp of evaderNodes collect timestamp of evaderPursuer uses timestamp to get velocity vector and which node to contact nextPursuer uses timestamp to get velocity vector and which node to contact nextNodes communicate only with pursuerNodes communicate only with pursuer

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ConclusionsConclusions

The tiers systems can benefit from The tiers systems can benefit from hierarchal topologyhierarchal topology Super nodes are at the root of the treeSuper nodes are at the root of the tree

Ant based approachAnt based approach Use HMM to shift processing from the pursuer Use HMM to shift processing from the pursuer

to sensor networkto sensor network Pursuers queries the sensorsPursuers queries the sensors