Multi-target hypothetical track initiation based on motion characteristics

Yiwei Lv; Tingyao Xie; Feng Feng; Xinyue Ren; Shangyan Li

doi:10.1117/12.2681170

23 May 2023 Multi-target hypothetical track initiation based on motion characteristics

Yiwei Lv, Tingyao Xie, Feng Feng, Xinyue Ren, Shangyan Li

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264517 (2023) https://doi.org/10.1117/12.2681170
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Aiming at the problems of difficult track initiation and high missed detection rate in dense clutter environment, this paper presents a method of multi-target hypothetical track initiation based on motion characteristics. Most of the traditional track initiation algorithms use the sequential start method, which has a great dependence on time. In this paper, a variety of hypotheses are generated by directly judging the correlation of the point track data, and the final track information is obtained by screening the formed temporary track according to the kinematic characteristics of the target. Experiments show that this method not only greatly improves the accuracy of track initiation in dense clutter environment and reduces the missed detection rate, but also has strong adaptability to track initiation of massive data.

Citation Download Citation

Yiwei Lv, Tingyao Xie, Feng Feng, Xinyue Ren, and Shangyan Li "Multi-target hypothetical track initiation based on motion characteristics", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264517 (23 May 2023); https://doi.org/10.1117/12.2681170

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