Adaptive parameter particle CBMeMBer tracker for multiple maneuvering target tracking

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.

A Gaussian Process based Method for Multiple Model Tracking

“…MM based methods can achieve satisfactory tracking performance when the uncertainty is low. However, in some cases, such as when the rotation is a time-varying or doubly-stochastic process, a sufficiently large set of models with different parameters is required to cover the range of possible motion models, which leads to a high computation complexity [4]. To address this aspect, [4] and [5] incorporated adaptive parameter estimation methods into the Bayesian filter framework, where the unknown parameter is estimated by approximating its distribution with particles and corresponding weights.…”

“…However, in some cases, such as when the rotation is a time-varying or doubly-stochastic process, a sufficiently large set of models with different parameters is required to cover the range of possible motion models, which leads to a high computation complexity [4]. To address this aspect, [4] and [5] incorporated adaptive parameter estimation methods into the Bayesian filter framework, where the unknown parameter is estimated by approximating its distribution with particles and corresponding weights. However, as parametric models are not always able to capture all aspects of the motion behaviours, those methods might fail if the chosen motion model set is incapable of modelling the ground truth trajectory.…”