Results 131 to 140 of about 355 (164)

Improved CPHD Filter for Multitarget Tracking

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2010
Cheng Ouyang, Hong-Bing Ji
exaly   +2 more sources

PHD and CPHD Filtering With Unknown Detection Probability

IEEE Transactions on Signal Processing, 2018
A 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, Wenguang Wang, Thia Kirubarajan, Jinping Sun, Peng Lei   +4 more
openaire   +1 more source

Gaussian mixture CPHD filter with gating technique

Signal Processing, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hongjian Zhang, Zhongliang Jing, Shiqiang Hu   +2 more
openaire   +1 more source

Linear-complexity CPHD filters

2010 13th International Conference on Information Fusion, 2010
The 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).
openaire   +2 more sources

Tracking multiple speakers using CPHD filter

Proceedings of the 15th ACM international conference on Multimedia, 2007
In this paper, we present an efficient method for tracking multiple speakers in a reverberant environment. The proposed method is based on the cardinalized probability hypothesis density (CPHD) filter. Because the CPHD filter can handle a large amount of clutter measurements, our method has a high reliability when tracking multiple speakers. Simulation
Nam Trung Pham, Weimin Huang 0002, S. H. Ong 0001   +2 more
openaire   +1 more source

A fast implementation of distributed fusion with CPHD filter

2017 International Conference on Control, Automation and Information Sciences (ICCAIS), 2017
The paper addresses a fast implementation algorithm about distributed fusion with CPHD filter. An implementation method of distributed fusion based on maximum probability association (MPA), called MPA-DF, is presented. Though the performance of MPA-DF is a little worse than traditional Generalization Covariance Intersection (GCI) distributed fusion ...
Guchong Li, Wei Yi 0002, Lingjiang Kong
openaire   +1 more source

Approximate multisensor CPHD and PHD filters

2010 13th International Conference on Information Fusion, 2010
The probability hypothesis density (PHD) filter and cardinalized probability hypothesis density (CPHD) filter are principled approximations of the general multitarget Bayes recursive filter. Both filters are single-sensor filters. Since their multisensor generalizations are computationally intractable, a further approximation-iterating their corrector ...
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Generalized CPHD filter modeling spawning targets

Signal Processing, 2016
In 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, Jiangwei Zou, Yu Duan, Shiyou Xu, Zengping Chen   +4 more
openaire   +1 more source

CPHD Filtering With Unknown Clutter Rate and Detection Profile

IEEE Transactions on Signal Processing, 2011
In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in multi-target filters such as the probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters.
Ronald P. S. Mahler, Ba-Tuong Vo, Ba-Ngu Vo   +2 more
openaire   +3 more sources

CPHD filters for superpositional sensors

SPIE Proceedings, 2009
The probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters were introduced as approximations of the full multitarget Bayes detection and tracking filter. Both filters are based on the "standard" multitarget measurement model that underlies most multitarget tracking theory. That is, sensor measurements are presumed to be detections.
openaire   +1 more source