DOC PREVIEW
CONCIE TRACK CHARACTERIZATION OF MANEUVERING TARGETS

This preview shows page 1-2-3-4-5-6 out of 17 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 17 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 17 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 17 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 17 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 17 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 17 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 17 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

Concise Track Characterization of Maneuvering TargetsProblem ContextResearch GoalsApproach - Segmenting Track Identifier (STI)Target modelsLinking coordinated turnsPosition and velocity continuityKnot Placement ApproachKnot placement flow diagramCost functionsCosts for joining segmentsTwo Segment ExampleEstimated weaving tracksTurn rate estimates for looping trackCumulative average RMS error for looping trackEstimation error vs. sample size and measurement noiseRemaining Tasks…February 2001 SUNY PlattsburghConcise Track Characterization of Maneuvering Targets Stephen LinderMatthew RyanRichard Quintin This material is based on work supported by Dr. Teresa McMullen through the Office of Naval Research underContract No. N00039-D-0042, Delivery Order No. D.O. 278.February 2001 SUNY PlattsburghProblem ContextA weaving target track constructed of linked coordinated turnsFebruary 2001 SUNY PlattsburghResearch Goals Improve instantaneous estimation of target velocity and acceleration for use by guidance law.Perform data compression on track data so that a succinct description of target track can be obtained “Target traveled at heading of 20° for 100 yards; Turned left at 10°/sec to heading of 100°”Use track characterization to dynamically select and tune guidance law parametersClassification of target from pattern of motion Computational feasibility for a real-time in-water systemFebruary 2001 SUNY PlattsburghApproach - Segmenting Track Identifier (STI)Support multiple localized nonlinear models of target motionMost current tracking techniques require linear motion models Use batch processing of dataDo not attempt to calculate globally optimal solution, ratherGenerate locally optimal track segments byminimizing mean square error of each track segment, and thenmatching the position and velocity of consecutive segments at the knots connecting the segmentsFebruary 2001 SUNY PlattsburghTarget modelsTarget models used by current trackersTurns (maneuvers) are modeled by the Singer maneuver modelManeuvers are time correlated with a specified time constant and acceleration varianceLocally linear models of coordinated models STI target modelTarget runs at only several discrete speedsTarget performs only coordinated turn maneuvers Continuity in position and velocity between segmentsFebruary 2001 SUNY PlattsburghLinking coordinated turnsknotsFebruary 2001 SUNY PlattsburghPosition and velocity continuityMatch positionMatch velocityFebruary 2001 SUNY PlattsburghKnot Placement ApproachPhase I – initial segmentationCalculate if knot is needed after every measurementPlace knot if RMSE error of current spline begins to increaseErr on the side of generating two many knots and then recombine knots in second phase of processingMake initial position, velocity and acceleration estimate Phase II – refine segmentationAfter second knot is placed go back and search for a knot position that optimizescontinuity conditions for position and velocity of the splines at knot, andminimize total least square fit of both splines to measured dataFebruary 2001 SUNY PlattsburghKnot placement flow diagram No YesAcquire new segment Optimize knot between Sn-2 and Sn-1Can segment Sn-1 and Sn-2 be merged?Merge successive segmentsSnSn-1Sn-2SnSn-1Sn-2SnSn-2Sn-1Sn-3SnSn-2Sn-1Sn-3SnSn-2Sn-1Sn-3Optimize knot between Sn-1 and SnFebruary 2001 SUNY PlattsburghCost functionsThe total least squares term for a line segment QL isThe total least squares term for circular arc segment QA is221 1pi iLiy mx bQm=� �- -=� �+� ��2122)(( )pcAi ciyyQ rx xi=� �-= + --� �� ��February 2001 SUNY PlattsburghCosts for joining segmentsThe C0 and C1 continuity condition is given by  is the difference in position at the knot between the n and n+1 segment  is the difference in heading at the knot between the n and n+1 segment kp is a proportionality constant based on the length of the diagonal of the spline’s bounding box ( )1, , 1 1, , 1( )C p n n n n n n n nQ n k f- + - += D +D +D +DR R, 1n n+DR, 1n nf+DFebruary 2001 SUNY PlattsburghTwo Segment Example-5-30 -20 -10 010-15-1051015-30-25-200X - positionX - position-30 -20 -10 0 1020-30-25-20-15-10-5051015X - positionY - positionSingle Trial 20 TrialsMeasurement Noise STD = 2.0truthKalman filter tracksKalman filter tracktruthSTI trackSTI tracksFebruary 2001 SUNY PlattsburghEstimated weaving tracksNoisy MeasurementsTrack EstimatesKalman Filter TrackSTI TrackFebruary 2001 SUNY PlattsburghTurn rate estimates for looping track0 10 20 30 40 50 60 70-40-35-30-25-20-15-10-50510X- positionY- position0 5 10 15 20 25 30-1-0.8-0.6-0.4-0.200.20.40.60.81Time (seconds)Turn rateTruthSTIKalman filterEstimated TracksEstimated Turn RatesFebruary 2001 SUNY PlattsburghCumulative average RMS error for looping trackRMS ErrorKalman filter-based tracker STI Trackerposition 1.56 0.64velocity 2.79 0.91acceleration 3.41 1.52900 trials of the five-maneuver track for a combination •measurement noise STD: 0.5, 1.0 and 2.0•sample sizes: 60, 120 and 180.February 2001 SUNY PlattsburghEstimation error vs. sample size and measurement noise0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 0.5 1 1.5 2 Measurement Error Velocity Error STI Algorithm 60 samples 120 samples 180 samples 180 samples 60s samples 120 samples Baseline Algorithm Diameter of the circle represents the RMS acceleration estimation errorKalman Filter Tracker with Singer Maneuver ModelSegmenting Track Identifier (STI)RMS error from 100 trials with looping tracksFebruary 2001 SUNY PlattsburghRemaining Tasks…Continue to refine algorithm Develop cost functions for range/bearing measurements.Support fusion of passive, active and Doppler processed active sonar data.Develop multiple track version of the tracker.Compare performance with Kalman filter-based trackerCharacterize lags in detecting maneuvers and performance with very sparse data.Extract track properties and integrate with guidance law and use track characterization to improve guidance


CONCIE TRACK CHARACTERIZATION OF MANEUVERING TARGETS

Download CONCIE TRACK CHARACTERIZATION OF MANEUVERING TARGETS
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view CONCIE TRACK CHARACTERIZATION OF MANEUVERING TARGETS and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view CONCIE TRACK CHARACTERIZATION OF MANEUVERING TARGETS 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?