On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
A Consistent Metric for Performance Evaluation of Multi-Object Filters
IEEE Transactions on Signal Processing - Part I
Joint Initialization and Tracking of Multiple Moving Objects Using Doppler Information
IEEE Transactions on Signal Processing
High-speed Sigma-gating SMC-PHD filter
Signal Processing
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In this paper, we propose a novel implementation of the probability hypothesis density (PHD) filter based on the sequential Monte Carlo (SMC) method called SMC-PHD filter. The SMC-PHD filter is analogous to the sequential importance sampling which generates samples using an importance sampling (IS) function. Even though this filter permits general class of IS density function, many previous implementations have simply used the state transition density function. However, this approach leads to a degeneracy problem and renders the filter inefficient. Thus, we propose a novel IS function for the SMC-PHD filter using a combination of an unscented information filter and a gating technique. Further, we use measurement-driven birth target intensities because they are more efficient and accurate than selecting birth targets selected using arbitrary or expected mean target states. The performance of the SMC-PHD filter with the proposed IS function was subsequently evaluated through a simulation and it was shown to outperform the standard SMC-PHD filter and recently proposed auxiliary PHD filter.