Results 231 to 240 of about 1,121,998 (275)

Gaussian mixture importance sampling function for unscented SMC-PHD filter

Signal Processing, 2013
The unscented sequential Monte Carlo probability hypothesis density (USMC-PHD) filter has been proposed to improve the accuracy performance of the bootstrap SMC-PHD filter in cluttered environments.
Du Yong Kim, Kuk-Jin Yoon
exaly   +3 more sources

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PHD filter with diffuse spatial prior on the birth process with applications to GM-PHD filter

2010 13th International Conference on Information Fusion, 2010
This paper presents a simple and efficient way to set the birth process of the Probability Hypothesis Density filter that enhances the performance of this approach when tracking multiple targets in clutter with no a priori spatial information on where targets can appear.
Jeremie Houssineau, Dann Laneuville
openaire   +2 more sources

The Bin-Occupancy Filter and Its Connection to the PHD Filters

IEEE Transactions on Signal Processing, 2009
An algorithm that is capable not only of tracking multiple targets but also of ldquotrack managementrdquo-meaning that it does not need to know the number of targets as a user input-is of considerable interest. In this paper we devise a recursive track-managed filter via a quantized state-space (ldquobinrdquo) model. In the limit, as the discretization
Ozgur Erdinc, Peter Willett 0001, Yaakov Bar-Shalom   +2 more
openaire   +1 more source

Convergence results for the particle PHD filter

IEEE Transactions on Signal Processing, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniel Edward Clark, Judith Bell
openaire   +2 more sources

Von Mises Mixture PHD Filter

IEEE Signal Processing Letters, 2015
This paper deals with the problem of tracking multiple targets on the unit circle, a problem that arises whenever the state and the sensor measurements are circular, i.e. angular-only, random variables. To tackle this problem, we propose a novel mixture approximation of the probability hypothesis density filter based on the von Mises distribution, thus
Ivan Markovic, Josip Cesic, Ivan Petrovic   +2 more
openaire   +2 more sources

Particle-gating SMC-PHD filter

Signal Processing, 2017
The Sequential Monte Carlo (SMC) implementation for the probability hypothesis density (PHD) filter, referred to as the SMC-PHD filter, is a good candidate for multi-target tracking (MTT) problems. It recursively propagates the weighted particle set that approximates the multi-target posterior density.
Yiyue Gao, Defu Jiang, Ming Liu 0019
openaire   +2 more sources

Data Association for the PHD Filter

2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 2005
The Probability Hypothesis Density (PHD) filter was developed as a suboptimal method for tracking a time varying number of targets. The first order statistical moment of the multiple target posterior distribution, called the Probability Hypothesis Density, gives the expected locations of the targets.
D.E. Clark, J. Bell
openaire   +1 more source

An alternative form of cardinalized PHD filter or I.I.D.-approximation filter

2007 10th International Conference on Information Fusion, 2007
In this paper, we derive the updating formula of the cardinalized probability hypothesis density (CPHD) filter recently developed in the works of Mahler et al., (2006) from the non- Poisson multiple-hypothesis tracking (MHT) algorithm developed earlier in the works of Mori et al. (2004).
Shozo Mori, Chee-Yee Chong
openaire   +2 more sources

Adaptive Retrodiction Particle PHD Filter for Multiple Human Tracking [PDF]

IEEE Signal Processing Letters, 2016
The probability hypothesis density (PHD) filter is well known for addressing the problem of multiple human tracking for a variable number of targets, and the sequential Monte Carlo (SMC) implementation of the PHD filter, known as the particle PHD filter,
Pengming Feng, Syed Mohsen Naqvi, Jonathon Chambers   +2 more
exaly   +2 more sources

Performance of PHD Based Multi-Target Filters

2006 9th International Conference on Information Fusion, 2006
The probability hypothesis density (PHD) recursion is a first moment approximation to the multi-target Bayes filter which propagates the posterior intensity of the random finite set of targets in time. The cardinalized PHD (CPHD) recursion is a generalization of the PHD recursion, which jointly propagates the posterior intensity and the posterior ...
Ba-Tuong Vo, Ba-Ngu Vo, Antonio Cantoni
openaire   +1 more source