A Labeled SMC-Cbmember Filter with Adaptive Track Initiation

Luo, Muyang; Wu, Xiaobiao; Sun, Hongyu; Xie, Xin; Wu, Changfei

doi:10.1088/1757-899x/452/4/042203

Cited by 1 publication

(1 citation statement)

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“…At the same time, scholars have put forward a variety of solutions to the track initiation problem under different conditions, for instance, narrowband target tracking situation [9], a low observable target with multipath measurements [10], closely spaced objects in the presence of clutter [11], the new target appearing in the detected area Luo et al [12]. These conditions are much closer to the real world, and the track initiation problem under these conditions becomes more complex.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Track Initiation in Heavy Clutter

Li¹,

Lou²,

Zhang³

et al. 2022

Computers, Materials &Amp; Continua

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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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Section: Introductionmentioning

confidence: 99%