Results 141 to 150 of about 355 (164)

CPHD filtering with unknown probability of detection

SPIE Proceedings, 2010
The conventional PHD and CPHD filters presume that the probability pD(x) that a measurement will be collected from a target with state-vector x (the state-dependent probability of detection) is known a priori. However, in many applications this presumption is false.
Ronald Mahler, Adel El-Fallah
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Adaptive Target Birth Intensity for PHD and CPHD Filters

IEEE Transactions on Aerospace and Electronic Systems, 2012
The standard formulation of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters assumes that the target birth intensity is known a priori. In situations where the targets can appear anywhere in the surveillance volume this is clearly inefficient, since the target birth intensity needs to cover the entire state space. This paper
Branko Ristic 0001, Daniel E. Clark, Ba-Ngu Vo, Ba-Tuong Vo   +3 more
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A survey of PHD filter and CPHD filter implementations

SPIE Proceedings, 2007
The probability hypothesis density (PHD) filter has attracted increasing interest since the author first introduced it in 2000. Potentially practical computational implementations of this filter have been devised, based on sequential Monte Carlo or on Gaussian mixture techniques.
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A novel track maintenance algorithm for PHD/CPHD filter

Signal Processing, 2012
Probability hypothesis density (PHD) filter and cardinalized PHD (CPHD) filter have proved to be promising algorithms for tracking an unknown number of targets in real time. However, they do not provide the identities of the individual estimated targets, so the target tracks cannot be obtained.
Jinlong Yang, Hongbing Ji
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Box-particle CPHD filter for multi-target tracking

2015 International Conference on Control, Automation and Information Sciences (ICCAIS), 2015
A 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.
Meng Liang, Liping Song, Hongbing Ji, Yufei Wang   +3 more
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Unified sensor management using CPHD filters

2007 10th International Conference on Information Fusion, 2007
The PHD filter propagates a multitarget statistical first moment, the probability hypothesis density (PHD), in place of the full multitarget posterior distribution. It has been the basis of a systematic approach to multisensor, multitarget sensor management based on the posterior expected number of targets (PENT) objective function.
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CPHD filter addressing occlusions with pedestrians and vehicles tracking

2013 IEEE Intelligent Vehicles Symposium (IV), 2013
In this paper, the problem of targets road tracking, like pedestrians and vehicles tracking is addressed. This paper proposes to improve a Cardinalized Probability Hypothesis Density (CPHD) filter in presence of occlusion using the sensor classification of each targets detected.
Laetitia Lamard, Roland Chapuis, Jean-Philippe Boyer   +2 more
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Hybrid multi-Bernoulli and CPHD filters for superpositional sensors

IEEE Transactions on Aerospace and Electronic Systems, 2015
In this paper we present an approximate multi-Bernoulli filter and an approximate hybrid multi-Bernoulli cardinalized probability hypothesis density filter for superpositional sensors. The approximate-filter equations are derived by assuming that the predicted and posterior multitarget states have the same form and propagating the probability ...
Santosh Nannuru, Mark Coates
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Improved CPHD filtering with unknown clutter rate

Proceedings of the 10th World Congress on Intelligent Control and Automation, 2012
To accommodate the model mismatch in clutter rate, a cardinality probability hypothesis density (CPHD) filter with unknown clutter rate has been proposed by Mahler. It has proved to be a promising algorithm for multi-target tracking in complex environment. However, in Mahler's algorithm, the calculation of the number of clutters without observations is
Xuetao Zheng, Liping Song
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The trajectory CPHD filter for spawning targets

Signal Processing, 2023
Boxiang Zhang, Wei Yi 0002, Lingjiang Kong   +2 more
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