Student-t Mixture GLMB Filter with Heavy-tailed Noises

Hu, Xiaolong; Zhang, Qin; Song, Baojun; Zhao, Mengxiao; Xia, Zhiquan

doi:10.1109/icspcc55723.2022.9984381

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Random finite sets can solve the complex relationships of data in multitarget tracking. The commonly used filtering methods for random finite sets include probability hypothesis density (PHD) [ 1 , 2 , 3 , 4 ], penalized probability hypothesis density (CPHD) [ 5 , 6 , 7 , 8 ], generalized labeled multi-Bernoulli (GLMB) [ 9 , 10 , 11 , 12 ], and CPHD optimization algorithms, such as those used by Xu, W. (2023) [ 13 ], who proposed the Gaussian mixture (GM) implementation of HMB-CPHD filters in their research. Kim, S. Y.…”

Section: Introductionmentioning

confidence: 99%

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The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection

Xu,

Wei,

Qin

et al. 2024

Sensors

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Some fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low. In response to the above issue, this study proposes a distributed Gaussian mixture cardinality jumping Markov-cardinalized probability hypothesis density (GM-JMNS-CPHD) filter based on a generalized inverse covariance intersection. The state estimation of the JMNS-CPHD filter combines the state evaluation of traditional CPHD filters with the state estimation of jump Markov systems, estimating the target state of multiple motion models without knowing the current motion models. The performances of the generalized covariance intersection (GCI)GCI-GM-JMNS-CPHD and generalized inverse covariance intersection (GICI)GICI-GM-JMNS-CPHD methods are evaluated via simulation results. The simulation results show that, compared with algorithms such as Sensor1, Sensor2, GCI-GM-CPHD, and GICI-GM-CPHD, this algorithm has smaller optimal subpattern assignment (OSPA) errors and a higher fusion accuracy.

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Section: Introductionmentioning

confidence: 99%

“…where ω j k is the weight of the mixed newborn intensity, m j k is the average of the mixed newborn intensity, and P j k is the covariance of the mixed newborn intensity. (11) where Q k−1 is the covariance matrix of the process noise; F k−1 is the state transition matrix; and the Gaussian probability density function with covariance is P.…”

mentioning

confidence: 99%

The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection

Xu,

Wei,

Qin

et al. 2024

Sensors

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show abstract

“…As a generalization of the ubiquitous Kalman filter, the Student's t-filter (STF) [26] models the process and measurement noises as Student's t-distributions with the same degrees of freedom (DOF) and then propagates the Student's t-based density, which can deal with these two noises in a natural manner. Based on this method, the Student's t mixture CBMeMBer (STM-CBMeMBer) filter [27], the STM-LMB filter [2], and the STM-GLMB filter [28] are proposed, especially the latter two filters, achieving higher estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Student’s t-Based Robust Poisson Multi-Bernoulli Mixture Filter under Heavy-Tailed Process and Measurement Noises

Zhu,

Xie,

Liu

2023

Remote Sensing

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A novel Student’s t-based robust Poisson multi-Bernoulli mixture (PMBM) filter is proposed to effectively perform multi-target tracking under heavy-tailed process and measurement noises. To cope with the common scenario where the process and measurement noises possess different heavy-tailed degrees, the proposed filter models this noise as two Student’s t-distributions with different degrees of freedom. Furthermore, this method considers that the scale matrix of the one-step predictive probability density function is unknown and models it as an inverse-Wishart distribution to mitigate the influence of heavy-tailed process noise. A closed-form recursion of the PMBM filter for propagating the approximated Gaussian-based PMBM posterior density is derived by introducing the variational Bayesian approach and a hierarchical Gaussian state-space model. The overall performance improvement is demonstrated through three simulations.

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Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration

Zhou,

Li,

Zhang

et al. 2024

Applied Sciences

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In most filtering algorithms involving measurement data association, handling the complex computations due to multiple hypotheses is necessary. This paper introduces a novel Trajectory Poisson Multi-Bernoulli (TPMB) filter for tracking extended targets, facilitated by a synergy between the model and the data. This filter can track extended targets under unknown process and measurement noise. Initially, on the model-driven side, we compute multi-model transition probabilities using the posterior probabilities from models at two consecutive time points with the targets in high maneuverability state. The accuracy of the tracking algorithm is improved by calculating the improved Interacting Multiple Model (IMM) transition probability at each time step. For the data-driven aspect, the Gate-control Belief Propagation (GBP) is set in the message- passing algorithm to reduce the running time of false hypothesis associations. Thus, it is unnecessary to consider all message information when computing the likelihood matrix for target-measurement associations. Subsequently, the posterior density function of the Adaptive Square Root Cubature Kalman Filter (ASCKF) is constructed to adaptively estimate unknown process and measurement noises, while importance sampling in the current particle filter further mitigates particle degradation. Experiments demonstrate that our algorithm reduces the running time of data associations, alleviates particle degradation, and more accurately tracks maneuvering targets under nonlinear conditions and estimates their states.

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Student-t Mixture GLMB Filter with Heavy-tailed Noises

Cited by 3 publications

References 16 publications

The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection

The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection

Student’s t-Based Robust Poisson Multi-Bernoulli Mixture Filter under Heavy-Tailed Process and Measurement Noises

Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration

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