Tracking and data association
Mathematics of Data Fusion
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
Convergence Analysis of the Gaussian Mixture PHD Filter
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
Multitarget miss distance via optimal assignment
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Tracking in a cluttered environment with probabilistic data association
Automatica (Journal of IFAC)
High-speed Sigma-gating SMC-PHD filter
Signal Processing
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Cardinalized probability hypothesis density (CPHD) filter provides more accurate estimates of target number than the probability hypothesis density (PHD) filter, and hence, also of the states of targets. This additional capability comes at the price of greater computational complexity: O(NM^3), where N is the number of targets and M is the cardinality of measurement set at each time index. It is shown that the computational cost of CPHD filter can be reduced by means of reducing the cardinality of measurement set. In practice, the cardinality of measurement set can be reduced by gating techniques as done in traditional tracking algorithms. In this paper, we develop a method of reducing the computational cost of Gaussian mixture CPHD filter by incorporating the elliptical gating technique. Computer simulation results show that the computational cost is reduced and that the tracking performance loss incurred is not significant.