Abstract
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 select the transition density as the importance sampling (IS) function, which is inefficient in a nonlinear scenario. To enhance the performance of the conventional SMC-PHD filter, we propose in this paper two approaches using the cubature information filter (CIF) for multi-target tracking. More specifically, we first apply the posterior intensity as the IS function. Then, we propose to utilize the CIF algorithm with a gating method to calculate the IS function, namely CISMC-PHD approach. Meanwhile, a fast implementation of the CISMC-PHD approach is proposed, which clusters the particles into several groups according to the Gaussian mixture components. With the constructed components, the IS function is approximated instead of particles. As a result, the computational complexity of the CISMC-PHD approach can be significantly reduced. The simulation results demonstrate the effectiveness of our approaches.
Highlights
We present the F-CISMC-probability hypothesis density (PHD) approach to reduce the computational complexity by considering groups of particles as Gaussian mixture components
We develop a fast version of the CISMC-PHD approach
We have proposed the CISMC-PHD and F-CISMC-PHD approaches, which can estimate the time-varying number and states of multi-target nonlinear tracking
Summary
Multi-target tracking refers to sequential approximation of the states (positions, velocities, etc.), and the number of individual targets. Caused by the targets appearing and disappearing, the state and observation sets of the multi-target are uncertain Such uncertainty costs high complexity in the conventional approaches on constructing the association. By propagating the first order moment (namely the intensity), the PHD filter can save much computational complexity in comparison to the optimal RFS-based Bayesian filter It avoids the combinatorial problem arising from data association, which is the bottleneck for conventional multi-target tracking approaches to estimate multi-target number and states. Inspired by the unscented particle filter, Yoon et al [25] utilized the unscented information filter (UIF) to design the IS function of SMC-PHD (called USMC-PHD filter) Since such a design takes the current observations of targets into account, it is more stable than the BSMC-PHD filter. Their method groups the particles in the update stage, enjoying the computational efficiency
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