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General solution and approximate implementation of the multisensor multitarget CPHD filter
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015Random finite set (RFS) based filters such as the cardinalized probability hypothesis density (CPHD) filter have been successfully applied to the problem of single sensor multitarget tracking. Various multisensor extensions of these filters have been proposed in the literature, but exact update equations for the multisensor CPHD filter have not been ...
Mark Coates +2 more
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Box-particle CPHD filter for multi-target tracking
2015 International Conference on Control, Automation and Information Sciences (ICCAIS), 2015A novel approach called box-particle cardinalized probability hypothesis density (BP-CPHD) filter for multi-target tracking is proposed in this paper. A box particle is a random sample that occupies a small and controllable rectangular region of nonzero volume in the target state space.
Hongbing Ji, Liping Song
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A tracker based on a CPHD filter approach for infrared applications
SPIE Proceedings, 2011 Since the derivation of PHD filter, a number of track management schemes have been proposed to adapt the PHD filter for determining the tracks of multiple objects. Nevertheless, the problem remains that such approaches can fail when targets are too close or are crossing.
Y. Petetin +3 more
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Improved CPHD Filter for Multitarget Tracking
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2010Cheng Ouyang
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CPHD filters with unknown quadratic clutter generators
SPIE Proceedings, 2015 Previous research has produced CPHD filters that can detect and track multiple targets in unknown, dynamically changing clutter. The .first such filters employed Poisson clutter generators and, as a result, were combinatorially complex. Recent research has shown that replacing the Poisson clutter generators with Bernoulli clutter generators results in ...
Ronald Mahler (23447857)
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An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation [PDF]
IEEE Journal on Selected Topics in Signal Processing, 2013 This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan.
Karl Granstrom +2 more
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PHD and CPHD Filtering With Unknown Detection Probability
IEEE Transactions on Signal Processing, 2018A priori knowledge of target detection probability is of critical importance in the Gaussian mixture probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. In addition, these two filters require that the process noise and measurement noise of the state propagated in the recursion be Gaussian.
Chenming Li +4 more
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Generalized CPHD filter modeling spawning targets
Signal Processing, 2016In some multiźtarget tracking applications, appearing targets are suitably modeled as spawning from existing targets. However, in the original cardinalized probability hypothesis density (CPHD) filter, this type of model is not included; instead appearing targets are modeled by spontaneous birth only.
Peiliang Jing +4 more
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Gaussian mixture CPHD filter with gating technique
Signal Processing, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hongjian Zhang +2 more
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Linear-complexity CPHD filters
2010 13th International Conference on Information Fusion, 2010The probability hypothesis density (PHD) filter and cardinalized probability hypothesis density (CPHD) filter are principled approximations of the general multitarget Bayes recursive filter. If n is the current number of tracks and m the current number of measurements, then the former has computational complexity O(mn) and the latter O(m3 n).
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