Refined PHD Filter for Multi-Target Tracking under Low Detection Probability

Wang, Sen; Bao, Qing; Chen, Zengping

doi:10.3390/s19132842

Cited by 17 publications

(17 citation statements)

References 41 publications

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“…Compared with multiple sparse targets, multiple targets in S2M have such situations as distance resolution blurring or overlapping occlusion, and the global data association algorithm can be adopted [146], [147].…”

Section: ) Single Sensor Tracks Multiple Targets (S2m)mentioning

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: ) Single Sensor Tracks Multiple Targets (S2m)mentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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“…Upon missed detection, the posterior particle weights are revised. False detection and real targets are distinguished with the help of the sequential probability ratio test [ 27 ]. Gao et al proposed a multi-frame GM-PHD filter to manage the weights of Gaussian components corresponding to undetected targets.…”

Section: Introductionmentioning

confidence: 99%

PHD Filter for Object Tracking in Road Traffic Applications Considering Varying Detectability

Törő

Bécsi

Gáspár

2021

Sensors

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This paper considers the object detection and tracking problem in a road traffic situation from a traffic participant’s perspective. The information source is an automotive radar which is attached to the ego vehicle. The scenario characteristics are varying object visibility due to occlusion and multiple detections of a vehicle during a scanning interval. The goal is to maintain and report the state of undetected though possibly present objects. The proposed algorithm is based on the multi-object Probability Hypothesis Density filter. Because the PHD filter has no memory, the estimate of the number of objects present can change abruptly due to erroneous detections. To reduce this effect, we model the occlusion of the object to calculate the state-dependent detection probability. Thus, the filter can maintain unnoticed but probably valid hypotheses for a more extended period. We use the sequential Monte Carlo method with clustering for implementing the filter. We distinguish between detected, undetected, and hidden particles within our framework, whose purpose is to track hidden but likely present objects. The performance of the algorithm is demonstrated using highway radar measurements.

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“…These filters are not multitarget trackers, which only estimate target states at individual time instants as opposed to multitarget trajectories. While these filters were not designed with the aim of estimating the trajectories of targets [ 10 ], they have been used in many applications [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets

Gao

Jiang

Zhang

et al. 2021

Sensors

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In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity.

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Refined PHD Filter for Multi-Target Tracking under Low Detection Probability

Cited by 17 publications

References 41 publications

Multi-Sensor Fusion in Automated Driving: A Survey

Multi-Sensor Fusion in Automated Driving: A Survey

PHD Filter for Object Tracking in Road Traffic Applications Considering Varying Detectability

A Labeled GM-PHD Filter for Explicitly Tracking Multiple Targets

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