Year: 2025
Type: article
Abstract: ABSTRACT The field of group target tracking is significantly advanced by the research on group target splitting and combination tracking. This study leverages the gamma Gaussian inverse Wishart probability hypothesis density (GGIW‐PHD) filter framework to delve into this area. A notable contribution is the introduction of an adaptive fast density peak algorithm, specifically designed to enhance the efficiency and effectiveness of the group target partitioning process, a critical component in tracking methodologies. Additionally, this paper introduces a tailored prediction fast density peak partition algorithm, optimized for nearest neighbour group targets. A novel separation model for group targets is also presented, incorporating measurement partition into the target separation process to augment the tracking accuracy of separated group targets. Furthermore, the integration of a separation decision condition based on measurement contributes to the overall stability of the tracking process. The simulation results emphatically demonstrate that the proposed algorithm outperforms existing methods, significantly elevating tracking performance while concurrently reducing tracking errors.
Source: The Journal of Engineering
DOI: 10.1049/tje2.70093
Authors: Tian Yuan, Yu Zhang, Yu Zhang, Zhengxin Ao, Peng Chen, Ying Chen, Yan Zhang, Yan Zhang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,000
Awards: G1319774170, G8638595564
OpenAlex ID: W4410613818