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Open AccessArticle
Improved Particle Filter Algorithm for Multi-Target Detection and Tracking
by
Yi Cheng
Yi Cheng ,
Wenbo Ren
Wenbo Ren *,
Chunbo Xiu
Chunbo Xiu and
Yiyang Li
Yiyang Li
School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(14), 4708; https://doi.org/10.3390/s24144708 (registering DOI)
Submission received: 21 June 2024
/
Revised: 17 July 2024
/
Accepted: 18 July 2024
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Published: 20 July 2024
Abstract
In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.
Share and Cite
MDPI and ACS Style
Cheng, Y.; Ren, W.; Xiu, C.; Li, Y.
Improved Particle Filter Algorithm for Multi-Target Detection and Tracking. Sensors 2024, 24, 4708.
https://doi.org/10.3390/s24144708
AMA Style
Cheng Y, Ren W, Xiu C, Li Y.
Improved Particle Filter Algorithm for Multi-Target Detection and Tracking. Sensors. 2024; 24(14):4708.
https://doi.org/10.3390/s24144708
Chicago/Turabian Style
Cheng, Yi, Wenbo Ren, Chunbo Xiu, and Yiyang Li.
2024. "Improved Particle Filter Algorithm for Multi-Target Detection and Tracking" Sensors 24, no. 14: 4708.
https://doi.org/10.3390/s24144708
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