cheap passive approximate localization · 2020-02-12 · • vhf: 88mhz-108mhz, bw: 200khz • less...

ì

UsingFMRadio

Cheap Passive Approximate localization

Jointworkwith:

AndreasAdolfsson,IntelligentRoboticsIncTathagataMukherjee,IntelligentRoboticsIncEduardoPasiliao,AFRL

PiyushKumarWebpage:compgeom.com/~piyush

LargeScaleLocalizationusingjustRSS

ReceivingMusic:Agoodantennaorheightmakesadifference

>40dbU

Localization

ì Defn:Toconfineorrestricttoaparticularlocality

ì But,IhaveaCellPhone!ì UnavailableGPSì Whatifyouareindoors?ì Whatifpowerwasaconcern?

ì (ComparedtoWifi/GPS)

ì Importantformanydifferentapplicationsincludingcommunication&navigationinGPSDeniedenvironments

=500mofnoGPS

But why FM?

Others:ADS-B,TV,ATC,…Ours:Twolevellocalizationsystem

ìFM LocalizationFMBroadcastSignal

Ø FMbroadcastband:• Largecoverage• Reliable• VHF:88MHz-108Mhz,BW:200kHz• Lesssensitivetoweathercondition

andindoorlimitationthanGPS

0 50 100 150 200 250

Range of FM Towers in miles

0

200

400

600

800

1000

1200

1400

Num

ber

ofFM

Tow

ers

Ø KSJS(FM90.5)60dBupolygoninSanJose,CA

ìFM Localization Methods

Ø RFbasedlocalizationtechniques• Algorithm:Beaconbased,Anchor

based,TimeofArrival,TimeDifferenceofArrival,AngleofArrival,Doppler

• Fingerprinting• WejustuseRSStolocalize

RSS Based Localization

Ø UsesRTLSDRforprototyping.

Ø CapableofscalingtoentirePlanet/US

Ø Simpleandscalablealgorithm

Ø ImprovesLocalization,bothindoorsandoutdoors

Ø Easytomakedistributed

Ø Workswithoutlineofsight

Ø NoSyncrequired

Test Data

ì Drovemultiplecars:350+Miles,multipledays

ì MeasuredFMSignalsatapproximately1000locations

Data Acquisition

ì CheapestRTLSoftwareDefinedRadio

Clock

Laptop

Data Acquisition

ì CheapestRTLSoftwareDefinedRadio

ì PowervsFrequencyplots

Power

Frequency

ìFM LocalizationSystemOverview

Ø PreprocessingPhase• MapGeneration

Ø QueryPhaseØ PeakFindingØ SubsetFilteringØ NearestNeighbors

ìFM LocalizationPreprocessingPhase

Ø Goal• Predictstheestimatedpower

atapointbasedonthepriorknowledgeofnearbyFMstation.

Ø MapGeneration• 40dBucoverage• EntireUSwithapproximately

2.4milex2.4milegrid• Powerspectrumineachgrid

ìFM LocalizationPreprocessingPhase

Ø Goal• Predictstheestimatedpower

atapointbasedonthepriorknowledgeofnearbyFMstation.

Ø MapGeneration• 40dBucoverage• EntireUSwithapproximately

2.4milex2.4milegrid• Powerspectrumineachgrid

ìFM LocalizationPreprocessingPhase

ìFM LocalizationPeakFindingPhase

Ø Powerspectrumatonelocation• Lookingfor“spikes”

alongthespectrum• Compareadjacent

signalstrengthwithathreshold

• Returnthechannelfrequencieswithpeak

ìFM LocalizationPeakFindingPhase

Ø Powerspectrumatonelocation• Lookingfor“spikes”

alongthespectrum• Compareadjacent

signalstrengthwithathreshold

• Returnthechannelfrequencieswithpeak

ìFM LocalizationSubsetFilteringPhase

Ø SubsetFiltering:SearchSpaceReduction• Goal:reduceinitialsearch

areadowntofewhundredsquaremiles

• GivenasetV,foranyqueryvectorq∈ {0,1},detectsifanyvectorp∈ Vsuchthatqisasubsetofp

ìFM LocalizationQueryPhase

Ø QueryPhase:Actuallocalizationalgorithm• Acquiresthepower

spectrum• Findingthepeaksinthe

acquiredpowerspectrum• InvokeSubsetFilter• Getaminimumvalue,which

indicatethedistancebetweentwospectrums,restrictedonagivensetofpeaksP.

• ReturnpredictedlocationfromGeohash

ìFM LocalizationEuclidianMetric/Calibrationchallenges

Ø Variability(BothTx andRx)• Time• Temperature• Humidity• ExperimentalError

Received Signal Strength (dBm)-64 -62 -60 -58 -56 -54 -52

Prob

abilit

y

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7102.3

fitted curve

Why min distance?

D_i =i-th ResultofthesubsetqueryX=PeaksatthelocationofinterestM=NumberofmatchesfromsubsetqueryL=Mostprobablelocation

ìFM LocalizationWhytheEuclidianMetric?

ìFM LocalizationFriis Model

Ø Friis Model:• Directreceivesignalstrength

calculation• 1700measurementsforloss

factorinTallahassee,FL• Assumingisotropic

transmission,ignoringmultipath/terraineffect

• Trilaterationfittingforcircle

FM 88.9

FM 94.9

FM 97.9FM 96.1

ìFM LocalizationFriis Model

FM 88.9

FM 94.9

FM 97.9FM 96.1

ìFM LocalizationResults

Ø EuclidianalgorithmwithGaussianprobabilityhastheminimumerrorcomparestoFriis ModelandKendall-TauModel

Improving accuracy in the air

https://youtu.be/DYP22RmxbQ8

Autonomous Data Collection

• DJIS1000+Frame+motors+ESCPixhawk autopilot+PX4firmware

• RTKGPSmodule

• FMAntenna

• i7NUCcomputer

• RTL-SDR+EttusB210

BluetoothSpeaker

Logitechc920Camera

Autonomous Data Collection

Afterdroneisarmed:• ChecksGPSaccuracy

• CollectsRSSIreadingonground

• Liftsto120metersintheair

• RemainsstationaryinairwhilecollectinganotherRSSIreading

• Lands

Collected30Datapoints• Accuracyofresultsimproveasdatapointsincrease

Data Processing

•Wefirstusethepreviousalgorithmtogetourerrorto5miles.

•Ourmodellearnstoestimatethedistancetothetransmittersfromagivenlocationusing:

•thetransmittedpower

•thereceivedpoweratthelocation

•theheightofthereceiver

•theheightaboveaverageterrain(HAAT)ofthetransmitter

WechoseaRandomForestregressionmodel,usingsupervisedlearningtechniquestoestimatethisdistancetoeachtransmitter.

randomforest/NeuralNet/SupportVectorMachine

Aerial Results

MinError:172meters,AverageError:3000meters.

Acknowledgements

ì AFRL

ì CompGeomInc.

ì IntelligentRoboticsInc.

ì EttusResearch

ì FloridaStateUniversity

Questions

ìFuture Work

Ø ImproveFMlocalizationaccuracy:• TDoA andAoA withdirectionalantenna• SimulatedDatabaseimprovement:

Splat!SimulationorRadioMap (DARPA)• Alternatemodalities:ADS-B,Iridium

SatelliteConstellation

Ø ComputerVisionforlocalizationandcollisionavoidance

FMLocalizationandRobotics