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As science develops, multi-target tracking technique advances towards dealing with complicated scenes, in which target number is time-varying and unknown, detection, measurement source and data association are uncertain. Among the new tracking methods, Mahler׳s Finite Set Statistics (FISST) based multi-target tracking technology naturally suits such complicated scenes. A performance metric with rigid...
Unification of the detection, tracking and classification for multiple targets is an object for random finite sets based filters developing. Introduction of target attribute information can improve tracking performance. Then, as the improved labeled particle probability hypothesis density (IL-P-PHD) filter is capable of joint detection and tracking, we will fuse obtained target attribute information...
A new multiple extended target tracking algorithm using the probability hypothesis density (PHD) filter is proposed in our study, to solve problems on tracking performance degradation of the extended target PHD (ET-PHD) filter under the nonlinear conditions and its intolerable computational requirement. It is noted that with the current Gaussian mixture implement of ET-PHD filter satisfying tracking...
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