Covariance control joint integrated probabilistic data association filter for multi‐target tracking

Zhu, Yun; Wang, Jun; Liang, Shuang; Wang, Jue

doi:10.1049/iet-rsn.2018.5142

Cited by 8 publications

(10 citation statements)

References 21 publications

(26 reference statements)

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“…If the score exceeds the threshold, the measurement is validated and updated as a track. Reference [161] combined the RFS theory and the advantages of JPDA, presents a covariance control JIPDA filter (CCJIPDA). Meanwhile, a detailed comparison of multi-target tracking algorithms, including JIPDA, ENNJIPDA, JIPDA*, RFS, and CCJIPDA.…”

Section: ) the Methods Based On Probabilistic Data Associationmentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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With the significant development of practicability in deep learning and the ultra-highspeed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.

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Section: ) the Methods Based On Probabilistic Data Associationmentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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“…To associate the newly detected target measurements with the existing target in the filter state, the data association method based on the global nearest neighbor (GNN) is applied [33]. To assign measurements to the corresponding track, a thresholding value is required to evaluate the simi- larity; an ellipsoidal gate is applied in this study [34]. The equation of the ellipsoidal gate can be written as…”

Section: ) Measurement Modelmentioning

confidence: 99%

Enhanced Target Ship Tracking With Geometric Parameter Estimation for Unmanned Surface Vehicles

Han¹,

Kim²,

Kim

2021

IEEE Access

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Autonomous collision detection and avoidance is a crucial requirement for the safe navigation of unmanned surface vehicles (USVs) in maritime traffic situations. Automatic identification system (AIS) is used to obtain the motion information of surrounding ships and their dimensional specifications. With AIS information, appropriate collision risk assessment between two ships can be performed; further, collision avoidance can be achieved by defining a safe radius of avoidance, which can be determined considering the shape parameters of a target ship. However, AIS data are often unreliable and some commercial fishing vessels intentionally turn off the public tracking system to hide their location. Under these circumstances, marine radars are used to detect and estimate the motion information of nearby ships. However, most existing target tracking studies model the target as a point object without any spatial extent, and thus, its physical dimensions cannot be identified. In this paper, a target tracking method that uses a marine radar is proposed to simultaneously estimate the motion states (i.e., position, course, and speed) of a target ship and its geometric parameter (length) in the framework of an extended Kalman filter (EKF). The proposed approach enhances collision avoidance by providing the kinematic and length parameters to evaluate collision risk and generate an appropriate collision-free path. Real-sea experiments using a developed USV system were conducted to verify and demonstrate the feasibility of the proposed algorithm; the results are presented and discussed in this paper.

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“…In this case, the posterior density becomes multimodal and the estimation of the multi-target state becomes less accurate. The trace of the covariance matrix is a measure of the multimodality of the posterior density [10,11]. To improve the accuracy of the multi-target state estimation, we used the evolutionary computation approach to minimize the trace of the covariance matrix and optimize the posterior density.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Optimization Based Set Joint Integrated Probabilistic Data Association Filter

Liang

Zhu

2022

Electronics

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The joint integrated probabilistic data association (JIPDA) algorithm is widely used for the automatic tracking of multiple targets, but it has the well-known problem of track coalescence. By optimizing the posterior density, the accuracy of the target state estimation can be improved. Motivated by this idea, we developed a novel evolutionary optimization based joint integrated probabilistic data association (EOJIPDA) filter to overcome the coalescence problem of the JIPDA filter. The trace for the covariance matrix of the posterior density is used as the objective function for the above optimization problem. It is shown that the accuracy of the target state estimation can be improved by reducing the trace. Evolutionary optimization was employed to minimize the trace and optimize the posterior density. More specifically, we enumerated all the possible permutations of the targets and assign a unique index to each permutation. The resulting indices were randomly assigned to all possible association hypothesis events. Each assignment indicated one possible gene in the evolutionary algorithm. This process was repeated several times to arrive at the initial population. An illustrative example shows that the EOJIPDA filter can effectively improve the accuracy of state estimation. Numerical studies are presented for two challenging multi-target tracking scenarios with clutter and missed detections. The experimental results demonstrate that the EOJIPDA filter provides better tracking accuracy than traditional coalescence-avoiding methods.

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Covariance control joint integrated probabilistic data association filter for multi‐target tracking

Cited by 8 publications

References 21 publications

Multi-Sensor Fusion in Automated Driving: A Survey

Multi-Sensor Fusion in Automated Driving: A Survey

Enhanced Target Ship Tracking With Geometric Parameter Estimation for Unmanned Surface Vehicles

Evolutionary Optimization Based Set Joint Integrated Probabilistic Data Association Filter

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