Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
The cardinality balanced multi-target multi-Bernoulli filter and its implementations
IEEE Transactions on Signal Processing
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
A Consistent Metric for Performance Evaluation of Multi-Object Filters
IEEE Transactions on Signal Processing - Part I
Efficient Multitarget Visual Tracking Using Random Finite Sets
IEEE Transactions on Circuits and Systems for Video Technology
Joint Initialization and Tracking of Multiple Moving Objects Using Doppler Information
IEEE Transactions on Signal Processing
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The unscented sequential Monte Carlo probability hypothesis density (USMC-PHD) filter has been proposed to improve the accuracy performance of the bootstrap SMC-PHD filter in cluttered environments. However, the USMC-PHD filter suffers from heavy computational complexity because the unscented information filter is assigned for every particle to approximate an importance sampling function. In this paper, we propose a Gaussian mixture form of the importance sampling function for the SMC-PHD filter to considerably reduce the computational complexity without performance degradation. Simulation results support that the proposed importance sampling function is effective in computational aspects compared with variants of SMC-PHD filters and competitive to the USMC-PHD filter in accuracy.