A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers

Liu, Zhuowei; Chen, Shuxin; Wu, Hao; He, Renke; Lin, Hui

doi:10.3390/s18041095

Cited by 10 publications

(5 citation statements)

References 25 publications

(40 reference statements)

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“…Two types of approaches are commonly used: Student's t-distribution PHD filter and variational Bayesian inference. For the PHD filter method based on Student's t-distribution [21], Liu Z proposed a PHD filter algorithm, modeling heavy-tailed noise with Student's t-distribution, simultaneously expressing the same posterior probability density distribution as Student's t-distribution alone. Later, the method was extended to nonlinear scenarios [22].…”

Section: Introduction 1literature Review and Motivationmentioning

confidence: 99%

A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian–Student’s t-Mixture Model

Wei,

Lin,

Wang

et al. 2024

Remote Sensing

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To realize multitarget trajectory tracking under non-Gaussian heavy-tailed noise, we propose a Gaussian–Student t-mixture distribution-based trajectory cardinality probability hypothesis density filter (GSTM-TCPHD). We introduce the multi-sensor GSTM-TCPHD (MS-GSTM-TCPHD) filter to enhance tracking performance. Conventional cardinality probability hypothesis density (CPHD) filters typically assume Gaussian noise and struggle to accurately establish target trajectories when faced with heavy-tailed non-Gaussian distributions. Heavy-tailed noise leads to significant estimation errors and filter dispersion. Moreover, the exact trajectory of the target is crucial for tracking and prediction. Our proposed GSTM-TCPHD filter utilizes the GSTM distribution to model heavy-tailed noise, reducing modeling errors and generating a set of potential target trajectories. Since single sensors have a limited field of view and limited measurement information, we extend the filter to a multi-sensor scenario. To tackle the issue of data explosion from multiple sensors, we employed a greedy approximation method to assess measurements and introduced the MS-GSTM-TCPHD filter. The simulation results demonstrate that our proposed filter outperforms the CPHD/TCPHD filter and Student’s t-based TCPHD filter in terms of accurately estimating the trajectories of multiple targets during tracking while also achieving improved accuracy and shorter processing time.

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Section: Introduction 1literature Review and Motivationmentioning

confidence: 99%

A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian–Student’s t-Mixture Model

Wei,

Lin,

Wang

et al. 2024

Remote Sensing

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show abstract

“…The Kalman filter (KF) is an optimal state estimator for linear state-space systems [1][2][3]. It is widely used, owing to its optimality, in many applications like, e.g., localization, control, target tracking, and signal processing [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In reality, however, systems are usually characterized by strong non-linearities which make the conventional KF inappropriate.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise

Dong

Chisci

Cai

2020

Sensors

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Aiming towards state estimation and information fusion for nonlinear systems with heavy-tailed measurement noise, a variational Bayesian Student’s t-based cubature information filter (VBST-CIF) is designed. Furthermore, a multi-sensor variational Bayesian Student’s t-based cubature information feedback fusion (VBST-CIFF) algorithm is also derived. In the proposed VBST-CIF, the spherical-radial cubature (SRC) rule is embedded into the variational Bayes (VB) method for a joint estimation of states and scale matrix, degree-of-freedom (DOF) parameter, as well as an auxiliary parameter in the nonlinear system with heavy-tailed noise. The designed VBST-CIF facilitates multi-sensor fusion, allowing to derive a VBST-CIFF algorithm based on multi-sensor information feedback fusion. The performance of the proposed algorithms is assessed in target tracking scenarios. Simulation results demonstrate that the proposed VBST-CIF/VBST-CIFF outperform the conventional cubature information filter (CIF) and cubature information feedback fusion (CIFF) algorithms.

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“…The RFS-based filters were widely applied in various tracking scenarios. In these scenarios, environment descriptions, e.g., the newborn target [ 11 , 12 , 13 , 14 , 15 ], the target motion models [ 16 , 17 ], the unknown target [ 18 , 19 ], the unknown process noise statistics [ 20 , 21 ], the unknown measurement noise statistics [ 22 , 23 ], the target measurement models [ 24 , 25 ], and the detection and clutter rate uncertainty [ 26 , 27 ], are of great importance. It is worth noting that the description of newborn targets attracted intensive attention over the recent years, which is typically called the target birth modeling.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Target Birth Intensity Multi-Bernoulli Filter with Noise-Based Threshold

Liu

2019

Sensors

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Adaptively modeling the target birth intensity while maintaining the filtering efficiency is a challenging issue in multi-target tracking (MTT). Generally, the target birth probability is predefined as a constant and only the target birth density is considered in existing adaptive birth models, resulting in deteriorated target tracking accuracy, especially in the target appearing cases. In addition, the existing adaptive birth models also give rise to a decline in operation efficiency on account of the extra birth modeling calculations. To properly adapt the real variation of the number of newborn targets and improve the multi-target tracking performance, a novel fast sequential Monte Carlo (SMC) adaptive target birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is proposed in this paper. Through adaptively conducting the target birth probability in a pre-processing step, which incorporates the information of current measurements to correct the pre-setting of the target birth probability, the proposed filter can truly adapt target birth cases and achieve better tracking accuracy. Moreover, the implementation efficiency can be improved significantly by employing a measurement noise-based threshold in the likelihood calculations of the multi-target updating. Simulation results verify the effectiveness of the proposed filter.

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A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers

Cited by 10 publications

References 25 publications

A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian–Student’s t-Mixture Model

A Robust TCPHD Filter for Multi-Sensor Multitarget Tracking Based on a Gaussian–Student’s t-Mixture Model

An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise

Adaptive Target Birth Intensity Multi-Bernoulli Filter with Noise-Based Threshold

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