Abstract
Cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter has been demonstrated as a promising algorithm for multi-target tracking, and the multi-model (MM) method has been incorporated into the CBMeMBer filter to solve the problem of multiple maneuvering target tracking. However, it is difficult to construct a proper set of models due to the unknown maneuvering parameters of the targets. Moreover, the number of models may increase exponentially if more unknown parameters have to be taken into account to match the target motion modes, which may lead to prohibitive computational complexity. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the framework of CBMeMBer filters, so that the model with unknown maneuvering parameter can be modified adaptively by using the selected parameter particles. Moreover, a particle labeling technique is introduced in the proposed algorithm in order to obtain the individual target track, which results in the adaptive parameter particle filter CBMeMBer (APPF-CBMeMBer) tracker. Simulation results show that the proposed algorithm can effectively track multiple maneuvering targets with abruptly changing parameters and exhibit better robustness than those of the well-known MM-based approaches.
Highlights
In recent years, random finite set (RFS) [1,2,3] is an elegant formulation of the multi-target tracking (MTT) problem and has generated substantial interest due to the development of the probability hypothesis density (PHD) filter [2] and the cardinalized PHD (CPHD) filter [3]
The existing closed-form solutions of PHD mainly include particle filter PHD (PF-PHD) [4, 5] and the Gaussian mixture PHD (GM-PHD) filter [6], which have opened the door to numerous novel extensions and applications as shown in [7,8,9,10,11,12,13]
The MMP-CBMeMBer filter [29] has a higher accuracy than the multiple-model PHD (MM-PHD) filter due to the fact that the multi-Bernoulli-based method propagates the parameterized approximation to the posterior cardinality distribution
Summary
Random finite set (RFS) [1,2,3] is an elegant formulation of the multi-target tracking (MTT) problem and has generated substantial interest due to the development of the probability hypothesis density (PHD) filter [2] and the cardinalized PHD (CPHD) filter [3]. In [25], a GM-PHD filter for jump Markov models is developed by employing the best-fitting Gaussian (BFG) approximation approach These algorithms assume the Gaussianity of the PHD distribution, which may limit the scope of their applications. We attempt to incorporate the adaptive parameter estimation (APE) technique into the framework of the CBMeMBer filter for addressing the problem of multiple maneuvering target tracking. The obtained adaptive parameter particle filter CBMeMBer (APPF-CBMeMBer) filter can track multiple maneuvering targets in the presence of unknown model parameters. It briefly reviews the APE technique and the CBMeMBer filter.
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