Performance Evaluation of Multi-platform Distributed Data Fusion Methods for Multi-target Tracking

Danu, Daniel; Sinha, Abhijit; Kirubarajan, T.; Farooq, M.; Peters, Daniel J.

doi:10.1109/aero.2007.353050

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…(3)(4)(5)(6)(7)(8)(9) and (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) shows that the fused state estimates from the convex combination fusion and Bhattacharya fusion are identical. The algorithm also ignores the cross-correlation between the local track estimates due to common process noise and prior information.…”

Section: Bhattacharya Fusion Algorithmmentioning

confidence: 89%

“…Use of (3)(4)(5)(6)(7)(8)(9)(10)(11) and (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) in (3-12) gives (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) ]. (3)(4)(5)(6)(7)(8)(9) and (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) shows that the fused state estimates from the convex combination fusion an...…”

Section: Bhattacharya Fusion Algorithmmentioning

confidence: 99%

“…The fused state estimate using the Bhattacharya fusion algorithm is described by (3)(4)(5)(6)(7)(8)(9)(10)(11)(12) , (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) .…”

Section: Bhattacharya Fusion Algorithmmentioning

confidence: 99%

“…The fused estimate and fused covariance according to the Bar-Shalom-Campo fusion algorithm [4], [5] are given, respectively, by (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) ), ( ) )( (…”

Section: Bar-shalom-campo Fusion Algorithmmentioning

confidence: 99%

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Comparison of track-to-track fusion algorithms using video sensors on multiple unmanned aerial vehicles

Mallick

Chang

2008

SPIE Proceedings

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Surveillance and ground target tracking using multiple electro-optical and infrared video sensors onboard unmanned aerial vehicles (UAVs) has drawn a great deal of interest in recent years. We compare a number of track-totrack fusion algorithms using a single target with the nearly constant velocity dynamic model and two UAVs. A local tracker is associated with each UAV and processes video measurements to produce local tracks. The video measurement is the centroid pixel location in the digital image corresponding to the target positions on the ground. In order to handle arbitrary height variations, we use the perspective transformation for the video measurement model. In addition, the video measurement model also includes radial and tangential lens distortions, scale, and offset. Since the video measurement model is a nonlinear function of the target position, the tracking filter uses a nonlinear filtering algorithm. A fusion center fuses track data received from two local trackers. The track-to-track fusion algorithms employed by the fusion center include the simple convex combination fusion, Bhattacharya fusion, Bar-Shalom-Campo fusion, and extended information filter based fusion algorithms. We compare the fusion accuracy, covariance consistency, bias in the fused estimate, communication load requirements, and scalability. Numerical results are presented using simulated data.

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Section: Bhattacharya Fusion Algorithmmentioning

confidence: 89%

Section: Bhattacharya Fusion Algorithmmentioning

confidence: 99%

“…The fused state estimate using the Bhattacharya fusion algorithm is described by (3)(4)(5)(6)(7)(8)(9)(10)(11)(12) , (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) .…”

Section: Bhattacharya Fusion Algorithmmentioning

confidence: 99%

Section: Bar-shalom-campo Fusion Algorithmmentioning

confidence: 99%

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Comparison of track-to-track fusion algorithms using video sensors on multiple unmanned aerial vehicles

Mallick

Chang

2008

SPIE Proceedings

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“…So far, much research has been done on asynchronous fusion algorithm of homogenous sensors and achieved substantial advances [4][5][6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

Asynchronous data fusion of infrared imaging and laser ranging for target tracking

Hao

Wang

2014

SPIE Proceedings

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In a multi-sensor system, sensors often provide data at different rates with different communication delays. To the asynchronous fusion for target tracking with infrared and laser sensors, that provide range and bearing information respectively, there mainly exist two problems need to be solved. One is temporal registration of different sensors since infrared sensor has a shorter sample time than laser sensor. The measurements are dealt with least square method, which makes full use of the high frequency characteristic of infrared sensor. The other is the choice of filtering algorithm because of the correlation of coordinate transformation when measurement equation is described in Cartesian coordinate.Converted measurement Kalman filter (CMKF) is a popular solution to the nonlinear estimation problem expect nonlinear filtering algorithm. Simulations of the algorithms adopted in the paper were carried out to track the target and the performance of different algorithms was compared. The results show that the algorithm based on CMKF yields better track performance than the fusion algorithm based on standard Kalman filter (KF) and a little worse than the algorithm based on unscented Kalman filter (UKF).

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Markov Decision Process-Based Resource and Information Management for Sensor Networks

Akselrod

Lang

McDonald³

et al. 2009

Signals and Communication Technology

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Performance Evaluation of Multi-platform Distributed Data Fusion Methods for Multi-target Tracking

Cited by 4 publications

References 11 publications

Comparison of track-to-track fusion algorithms using video sensors on multiple unmanned aerial vehicles

Comparison of track-to-track fusion algorithms using video sensors on multiple unmanned aerial vehicles

Asynchronous data fusion of infrared imaging and laser ranging for target tracking

Markov Decision Process-Based Resource and Information Management for Sensor Networks

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