Robust Measurement-Driven Cardinality Balance Multi-Target Multi-Bernoulli Filter [PDF]
Sensors, 2021 The multi-target tracking filter under the Bayesian framework has strict requirements on the prior information of the target, such as detection probability density, clutter density, and target initial position information.
Biao Yang +4 more
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Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter [PDF]
Sensors, 2021 This paper presents a Bayesian filter based solution to the Space Object (SO) tracking problem using simulated optical telescopic observations. The presented solution utilizes the Probabilistic Admissible Region (PAR) approach, which is an orbital ...
Leonardo Cament +2 more
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Cardinality Balanced Multi-Target Multi-Bernoulli Filter with Error Compensation [PDF]
Sensors, 2016 The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter developed recently has been proved an effective multi-target tracking (MTT) algorithm based on the random finite set (RFS) theory, and it can jointly estimate the number of targets ...
Xiangyu He, Guixi Liu
doaj +2 more sources
Robust Poisson Multi-Bernoulli Filter With Unknown Clutter Rate
IEEE Access, 2019 In Bayesian multi-target tracking (MTT), knowledge of clutter intensity is required for effective multi-target state estimation. In this paper, we propose an online multi-target filter that can operate under background with unknown clutter intensity. Our
Weijian Si, Hongfan Zhu, Zhiyu Qu
doaj +3 more sources
Multi-Target Tracking Based on Multi-Bernoulli Filter with Amplitude for Unknown Clutter Rate [PDF]
Sensors, 2015 Knowledge of the clutter rate is of critical importance in multi-target Bayesian tracking. However, estimating the clutter rate is a difficult problem in practice.
Changshun Yuan +4 more
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Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach. [PDF]
Biom JABSTRACT Categorical variables are of uttermost importance in biomedical research. When two of them are considered, it is often the case that one wants to test whether or not they are statistically dependent. We show weaknesses of classical methods—such as Pearson's and the G$G$‐test—and we propose testing strategies based on distances that lack those ...
Castro-Prado F +4 more
europepmc +2 more sources
Extended Target Fast Labeled Multi-Bernoulli Filter [PDF]
Radioengineering, 2023 Focusing on the real-time tracking of the extended target labeled multi-Bernoulli (ET-LMB) filter, this paper proposes an extended target fast labeled multi-Bernoulli (ET-FLMB) filter based on beta gamma box particle (BGBP) and Gaussian process (GP ...
X. Cheng, H. Ji, Y. Zhang
doaj
A Multisource Multi-Bernoulli Filter for Multistatic Radar
IEEE Access, 2022 Compared with conventional monostatic or bistatic radar, multistatic radar has wider coverage, better performance of localization and higher tracking accuracy.
Xueqin Zhou, Hong Ma, Jiang Jin, Hang Xu
doaj +1 more source
Adaptive Multi-target Tracking Algorithm with Unknown Detection Probability [PDF]
Jisuanji gongcheng, 2017 In order to accurately model the system detection probability in a complex background,a Multi-Target Tracking(MTT) method with unknown detection probability is proposed.The detection probability is modeled by the Time Varying AutoRegressive(TVAR) process.
LIU Jun,YUAN Peiyan,QIU Hao
doaj +1 more source
Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods
Sensors, 2017 We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association.
Anthony Hoak +2 more
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