Cross-entropy and rare events for maximal cut and partition problems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
The cardinality balanced multi-target multi-Bernoulli filter and its implementations
IEEE Transactions on Signal Processing
Ant clustering PHD filter for multiple-target tracking
Applied Soft Computing
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing - Part II
Complexity analysis of the marginalized particle filter
IEEE Transactions on Signal Processing
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
A Consistent Metric for Performance Evaluation of Multi-Object Filters
IEEE Transactions on Signal Processing - Part I
CPHD Filtering With Unknown Clutter Rate and Detection Profile
IEEE Transactions on Signal Processing
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Probability hypothesis density (PHD) filter and cardinalized PHD (CPHD) filter have proved to be promising algorithms for tracking an unknown number of targets in real time. However, they do not provide the identities of the individual estimated targets, so the target tracks cannot be obtained. To solve this problem, we propose a new track maintenance algorithm based on the cross entropy (CE) technique. Firstly, the particle filter PHD (PF-PHD) algorithm is used to estimate the target states and the target number. Then, the results of the estimation are used as vertexes to construct a connectivity graph with associated weights, and the CE technique is employed as a global optimization scheme to calculate the optimal feasible associated events. Furthermore, due to the advantages of the CPHD filter and the Rao-Blackwellized particle filter (RBPF), we propose another track maintenance algorithm based on the CE technique, named the RBPF-CPHD tracker, which can further improve the track maintenance performance due to the more accurate state estimates and their number estimates. Simulation results show that the proposed algorithms can effectively achieve the track continuity, with stronger robustness and greater anti-jamming capability.