Open Access

ARTICLE

Multi-Target Track Initiation in Heavy Clutter

Li Xu^1,2,*, Ruzhen Lou¹, Chuanbin Zhang¹, Bo Lang³, Weiyue Ding⁴

1 College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China
2 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
3 Norinco Group Air Ammunition Research Institute Co. Ltd., Harbin, 150036, China
4 Harvard Medical School, Boston, 02115, USA

* Corresponding Author: Li Xu. Email:

Computers, Materials & Continua 2022, 72(3), 4489-4507. https://doi.org/10.32604/cmc.2022.027400

Received 17 January 2022; Accepted 04 March 2022; Issue published 21 April 2022

Abstract

In the heavy clutter environment, the information capacity is large, the relationships among information are complicated, and track initiation often has a high false alarm rate or missing alarm rate. Obviously, it is a difficult task to get a high-quality track initiation in the limited measurement cycles. This paper studies the multi-target track initiation in heavy clutter. At first, a relaxed logic-based clutter filter algorithm is presented. In the algorithm, the raw measurement is filtered by using the relaxed logic method. We not only design a kind of incremental and adaptive filtering gate, but also add the angle extrapolation based on polynomial extrapolation. The algorithm eliminates most of the clutter and obtains the environment with high detection rate and less clutter. Then, we propose a fuzzy sequential Hough transform-based track initiation algorithm. The algorithm establishes a new meshing rule according to system noise to balance the relationship between the grid granularity and the track initiation quality. And a flexible superposition matrix based on fuzzy clustering is constructed, which avoids the transformation error caused by 0–1 voting method in traditional Hough transform. In addition, the algorithm allows the superposition matrixes of nonadjacent cycles to be associated to overcome the shortcoming that the track can’t be initiated in time when the measurements appear in an intermittent way. And a slope verification method is introduced to detect formation-intensive serial tracks. Last, the sliding window method is employed to feedback the track initiation results timely and confirm the track. Simulation results verify that the proposed algorithms can initiate the tracks accurately in heavy clutter.

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Keywords

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