Results 211 to 220 of about 175,081 (248)

A physical-space approach for the probability hypothesis density and cardinalized probability hypothesis density filters

SPIE Proceedings, 2006
The probability hypothesis density (PHD) filter, an automatically track-managed multi-target tracker, is attracting increasing but cautious attention. Its derivation is elegant and mathematical, and thus of course many engineers fear it; perhaps that is currently limiting the number of researchers working on the subject. In this paper, we explore
Ozgur Erdinc, Peter Willett, Yaakov Bar-Shalom   +2 more
openaire   +1 more source

A Multiple-Detection Probability Hypothesis Density Filter

IEEE Transactions on Signal Processing, 2015
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Tang, X., Chen, X., McDonald, M., Mahler, Ronald, Tharmarasa, R., Kirubarajan, T.   +5 more
openaire   +3 more sources

The forward-backward Probability Hypothesis Density smoother

2010 13th International Conference on Information Fusion, 2010
A forward-backward Probability Hypothesis Density (PHD) smoother involving forward filtering followed by backward smoothing is derived. The forward filtering is performed by Mahler's PHD recursion. The PHD backward smoothing recursion is derived using Finite Set Statistics (FISST) and standard point process theory. Unlike the forward PHD recursion, the
Ronald P. S. Mahler, Ba-Ngu Vo, Ba-Tuong Vo   +2 more
openaire   +2 more sources

Multitarget Tracking using Probability Hypothesis Density Smoothing

IEEE Transactions on Aerospace and Electronic Systems, 2011
In general, for multitarget problems where the number of targets and their states are time varying, the optimal Bayesian multitarget tracking is computationally demanding. The Probability Hypothesis Density (PHD) filter, which is the first-order moment approximation of the optimal one, is a computationally tractable alternative.
Nandakumaran Nadarajah, Thia Kirubarajan, Thomas Lang, Michael McDonald 0001, Kumaradevan Punithakumar   +4 more
openaire   +2 more sources

Passive infrared localization with a Probability Hypothesis Density filter

2010 7th Workshop on Positioning, Navigation and Communication, 2010
In passive infrared localization (PIL) humans are located based on their thermal radiation. Thus, an active tag is not required and privacy is guaranteed due to non-identifying sensors. However, in case of multi-target tracking, the non-identifying sensors result in missing associations between targets and measurements.
Jürgen Kemper, Daniel Hauschildt
openaire   +1 more source

Improved Gaussian mixture probability hypothesis density smoother

Signal Processing, 2016
The Gaussian mixture probability hypothesis density (GM-PHD) smoother proposed recently is a closed-form solution to the forward-backward PHD smoother for the linear Gaussian model, it can yield better state estimates than the GM-PHD filter. However, for the standard GM-PHD smoother, when one or more targets disappear during forward filtering, the ...
Xiangyu He, Guixi Liu
openaire   +1 more source

Improved cardinalized probability hypothesis density filtering algorithm

Applied Soft Computing, 2014
To overcome computerized intractability and imprecise estimation of the standard cardinalized probability hypothesis density (CPHD) filter for multitarget tracking (MTT), an improved CPHD filtering algorithm is proposed in this paper. We apply Sequential Monte Carlo (SMC) method to achieve the closed-form solution in the filtering process as well as to
Bo Li 0059, Fu-Wen Pang
openaire   +1 more source

The Probability Hypothesis Density filter with evidence fusion

Journal of Electronics (China), 2009
The original Probability Hypothesis Density (PHD) filter is a tractable algorithm for Multi-Target Tracking (MTT) in Random Finite Set (RFS) frameworks. In this paper, we introduce a novel Evidence PHD (E-PHD) filter which combines the Dempster-Shafer (DS) evidence theory.
Weifeng Liu, Xiaobin Xu
openaire   +2 more sources

Multiple model spline probability hypothesis density filter

IEEE Transactions on Aerospace and Electronic Systems, 2016
The probability hypothesis density (PHD) filter is an efficient algorithm for multitarget tracking in the presence of nonlinearities and/or non-Gaussian noise. The sequential Monte Carlo (SMC) and Gaussian mixture (GM) techniques are commonly used to implement the PHD filter.
Rajiv Sithiravel, Michael McDonald 0001, Bhashyam Balaji, Thiagalingam Kirubarajan   +3 more
openaire   +1 more source

A closed form solution to the Probability Hypothesis Density Smoother

2010 13th International Conference on Information Fusion, 2010
A closed form Gaussian mixture solution to the forward-backward Probability Hypothesis Density smoothing recursion is proposed. The key to the closed form solutions is the use of an alternative form of the backward propagation, together with terse yet suggestive notations that have natural interpretation in terms of measurement predictions.
Ba-Ngu Vo, Ba-Tuong Vo, Ronald P. S. Mahler   +2 more
openaire   +2 more sources