Multi-object Bayesian filter for jump Markov system under glint noise

Signal Processing ◽

10.1016/j.sigpro.2018.11.020 ◽

2019 ◽

Vol 157 ◽

pp. 131-140 ◽

Author(s):

Zong-xiang Liu ◽

Bing-jian Huang ◽

Yan-ni Zou ◽

Liang-qun Li

Keyword(s):

Bayesian Filter ◽

Markov System ◽

Jump Markov System

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A Gaussian Mixture PHD Filter for Jump Markov System Models

IEEE Transactions on Aerospace and Electronic Systems ◽

10.1109/taes.2009.5259174 ◽

2009 ◽

Vol 45 (3) ◽

pp. 919-936 ◽

Author(s):

Syed Ahmed Pasha ◽

Ba-Ngu Vo ◽

Hoang Duong Tuan ◽

Wing-Kin Ma

Keyword(s):

Gaussian Mixture ◽

System Models ◽

Markov System ◽

Jump Markov System

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A Kullback–Leibler-Based IMM Information Filter for the Jump Markov System with Unknown Noise

Circuits Systems and Signal Processing ◽

10.1007/s00034-017-0735-y ◽

2017 ◽

Vol 37 (9) ◽

pp. 4065-4081 ◽

Author(s):

Chen Shen ◽

Wei Huang

Keyword(s):

Markov System ◽

Information Filter ◽

Jump Markov System

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Gaussian Mixture PHD Filter with State-Dependent Jump Markov System Models

2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC) ◽

10.1109/dasc43569.2019.9081744 ◽

2019 ◽

Author(s):

Dohyeung Kim ◽

Inseok Hwang

Keyword(s):

Gaussian Mixture ◽

System Models ◽

Markov System ◽

State Dependent ◽

Jump Markov System

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A Multi-Target Tracker based on Gaussian Mixture PHD Filter with Jump Markov System Models

AIAA Scitech 2020 Forum ◽

10.2514/6.2020-1370 ◽

2020 ◽

Author(s):

Dohyeung Kim ◽

Inseok Hwang

Keyword(s):

Gaussian Mixture ◽

System Models ◽

Markov System ◽

Jump Markov System

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Tracking multiple maneuvering targets using a sequential multiple target Bayes filter with jump Markov system models

Neurocomputing ◽

10.1016/j.neucom.2016.07.028 ◽

2016 ◽

Vol 216 ◽

pp. 183-191 ◽

Author(s):

Zong-xiang Liu ◽

Qi-quan Zhang ◽

Liang-qun Li ◽

Wei-xin Xie

Keyword(s):

Multiple Target ◽

System Models ◽

Markov System ◽

Maneuvering Targets ◽

Jump Markov System

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Extensions of jump Markov system estimation techniques

Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301) ◽

10.1109/acc.2002.1024592 ◽

2002 ◽

Author(s):

Kunpeng Sun ◽

Lun Li ◽

G. Panayotopoulos ◽

A. Packard

Keyword(s):

Estimation Techniques ◽

Markov System ◽

Jump Markov System

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Efficient generalized labeled multi-bernoulli filter for jump Markov system

2017 International Conference on Control, Automation and Information Sciences (ICCAIS) ◽

10.1109/iccais.2017.8217580 ◽

2017 ◽

Author(s):

Yuthika Punchihewa

Keyword(s):

Markov System ◽

Bernoulli Filter ◽

Jump Markov System

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An improvement to the linear jump Markov system Gaussian mixture probability hypothesis density filter for maneuvering target tracking

2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) ◽

10.1109/iciea.2012.6361021 ◽

2012 ◽

Author(s):

Zhang Shicang ◽

Li Jianxun ◽

Wu Liangbin

Keyword(s):

Target Tracking ◽

Gaussian Mixture ◽

Maneuvering Target Tracking ◽

Probability Hypothesis Density ◽

Maneuvering Target ◽

Markov System ◽

Probability Hypothesis Density Filter ◽

Density Filter ◽

Jump Markov System

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On Rational Approximation of Markov Functions by Partial Sums of Fourier Series on a Chebyshev–Markov System

Mathematical Notes ◽

10.1134/s0001434620090291 ◽

2020 ◽

Vol 108 (3-4) ◽

pp. 566-578

Author(s):

E. A. Rovba ◽

P. G. Potseiko

Keyword(s):

Fourier Series ◽

Rational Approximation ◽

Partial Sums ◽

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A Recursive Bayesian Filter for Anomalous Behavior Detection in Trajectory Data

Connecting a Digital Europe Through Location and Place - Lecture Notes in Geoinformation and Cartography ◽

10.1007/978-3-319-03611-3_6 ◽

2014 ◽

pp. 91-104 ◽

Author(s):

Hai Huang ◽

Lijuan Zhang ◽

Monika Sester

Keyword(s):

Anomalous Behavior ◽

Trajectory Data ◽

Bayesian Filter ◽

Behavior Detection ◽

Recursive Bayesian Filter ◽

Recursive Bayesian

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