Cubature Information SMC-PHD for Multi-Target Tracking

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Cubature Information SMC-PHD for Multi-Target Tracking

In multi-target tracking, the key problem lies in estimating the number and states of individual targets, in which the challenge is the time-varying multi-target numbers and states. Recently, several multi-target tracking approaches, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, have been presented to solve such a problem. However, most of these approaches...

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The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been demonstrated to be a favorable method for multi-target tracking. However, the time-varying target states need to be extracted from the particle approximation of the posterior PHD, which is difficult to implement due to the unknown relations between the large amount of particles and the PHD peaks representing pot...

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The Gaussian mixture probability hypothesis density (GM-PHD) smoother proposed recently can yield better state estimates than the GM-PHD filter. However, there are two major problems with it. First, the smoothed PHD distribution can not provide a more accurate target number estimate due to the target number estimation bias becoming larger by smoothing. Second, the computational complexity of co...

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ژورنال

عنوان ژورنال: Sensors

سال: 2016

ISSN: 1424-8220

DOI: 10.3390/s16050653