Multiple extended target tracking by truncated JPDA in a clutter environment

Li, Qinlei; Song, Liping; Zhang, Yongquan

doi:10.1049/sil2.12024

Cited by 7 publications

(4 citation statements)

References 39 publications

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“…These algorithms are generally related to filtering algorithms in target tracking. For example, Probability Data Association Filter (PDAF), Joint Probability Data Association Filter (JPDAF), Multiple Hypothesis Tracking Filter (MHTF), Interacting Multiple Model Filter (IMMF), and Probabilistic Hypothesis Density Filter (PHDF) [1][2][3][4][5]. Except for the PDAF algorithm, other algorithms can be used to handle the problem of multi-target data association.…”

Section: Introductionmentioning

confidence: 99%

“…To solve these problems, scholars have proposed many related algorithms [6][7][8][9]. For example, a truncated JPDAF algorithm is proposed in reference [3], which can effectively filter the clutter in the data combined with the characteristics of target motion. In reference [4], fuzzy recursive least squares filtering is combined with JPDAF to achieve effective tracking of maneuvering targets in the environment where both the measurement error and the motion model are uncertain.…”

Section: Introductionmentioning

confidence: 99%

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Tracking and Data Association Based on Reinforcement Learning

Xiong

Cui

2023

Electronics

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Currently, most multi-target data association methods require the assumption that the target motion model is known, but this assumption is clearly not valid in a real environment. In the case of an unknown system model, the influence of environmental clutter and sensor detection errors on the association results should be considered, as well as the occurrence of strong target maneuvers and the sudden appearance of new targets during the association process. To address these problems, this paper designs a target tracking and data association algorithm based on reinforcement learning. First, this algorithm combines the dynamic exploration capability of reinforcement learning and the long-time memory function of LSTM network to design a policy network that predicts the probability of associating a point with its various possible source targets. Then, the Bayesian network and the multi-order least squares curve fitting method are combined to predict the location of target, and the results are fed into the Bayesian recursive function to obtain the reward. Simultaneously, some corresponding mechanisms are proposed for possible problems that interfere with the association process. Finally, the simulation experimental results show that this algorithm associates the results with higher accuracy compared to other algorithms when faced with the above problem.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tracking and Data Association Based on Reinforcement Learning

Xiong

Cui

2023

Electronics

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“…The traditional joint probability data association algorithm (JPDA) [39] determines the effective measurement fusion value by calculating the association probability between the measured value and multiple targets. This method does not need the prior information of targets and clutter and can track multiple targets effectively in a cluttered environment [40]. Therefore, it is always used in the research on point-track association.…”

Section: Introductionmentioning

confidence: 99%

A Novel Auxiliary Excretion Approach to a Lavatory Robot with Safety and Robustness

et al. 2022

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The excretion behavior in daily life for the elderly and the disabled is a high frequency, high physical load, and risky behavior. Therefore, we proposed an auxiliary lavatory robot (ALR) with autonomous movement and self-cleaning capability. When the nursing staff assists a user in transferring from a standing or lying state to sitting on the ALR, the ALR can follow the user according to their position and posture. Over the whole transfer process, the ALR always provides the user with the best transfer position and posture, which is an effective approach to reduce workload and physical load. However, confusion and occlusion of the lower limbs between the nursing staff and the user would affect the user’s posture recognition. First, in this paper, a method combined with object segmentation and shape constraint was proposed to extract the contour of the lower limbs of the user and the nursing staff. Then, depending on the position constraint and dynamic characteristics of the legs contour and back contour of the user, a dynamic posture recognition approach based on a two-level joint probabilistic data association algorithm (JPDA) was proposed. Finally, the leg target recognition experiment, path-tracking experiment, and auxiliary excretion transfer experiment were implemented to verify the effectiveness and robustness of our proposed algorithm. The experimental results showed that our proposed method improved the safety and convenience of the user, and it also reduced the workload and physical load of the nursing staff. The ALR, integrated with the proposed method, has a good universal property for the elderly and disabled with weak motion capability in hospitals, pension centers, and families.

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“…Therefore, traditional point-track association methods often involve filtering algorithms commonly used in target tracking, such as Probability Data Association (PDA) filter, Joint Probability Data Association (JPDA) filter, Multiple Hypothesis Tracking (MHT) filter, Interactive Multiple Model (IMM) filter and Probability Hypothesis Density (PHD) filter, etc. [1][2][3][4][5] Among these classical filter algorithms, the PDA filter algorithm can only be used to deal with the single-target point-track association problem, and other filter algorithms are also suitable for dealing with the multi-target point-track association problem. But, without exception, as long as it is a filter algorithm, it needs to assume that the system model (the state transition matrix of the system) is known.…”

Section: Introductionmentioning

confidence: 99%

Point‐track association method with unknown system model

Xiong¹,

Gu²,

Cui³

2022

IET Radar Sonar & Navi

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The point-track association methods are proposed on the premise that the system models are known, which obviously does not conform to the actual air target detection environment. Considering this situation, for the point-track association problems in clutter environment, a point-track association method with unknown system model (USMA) is proposed. The method integrates reinforcement learning (RL) theory and traditional point-track association framework, utilises the association process migration of different models, simplifies the entire learning process, and improves the generalisation ability by designing an adaptive mechanism. The experimental results show that when the system model is unknown, the USMA method can more accurately correlate to the measurements, and can also solve the problems of point-track association with a certain clutter density. Compared with other methods, the USMA method performs better.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Multiple extended target tracking by truncated JPDA in a clutter environment

Cited by 7 publications

References 39 publications

Tracking and Data Association Based on Reinforcement Learning

Tracking and Data Association Based on Reinforcement Learning

A Novel Auxiliary Excretion Approach to a Lavatory Robot with Safety and Robustness

Point‐track association method with unknown system model

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