Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

Qiang GUO, Long TENG, Xinliang WU, Wenming SONG, Dayu HUANG

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Front. Inform. Technol. Electron. Eng All Journals
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Front. Inform. Technol. Electron. Eng ›› 2022, Vol. 23 ›› Issue (12) : 1871-1880. DOI: 10.1631/FITEE.2200286
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Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

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Abstract

A novel algorithm that combines the generalized labeled multi-Bernoulli (GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments, emitter features (EFs) are often unknown and time-varying. Aiming at the unknown feature problem, we propose a method for identifying EFs based on dynamic clustering of data fields. Because EFs are time-varying and the probability distribution is unknown, an improved fuzzy C-means algorithm is proposed to calculate the correlation coefficients between the target and measurements, to approximate the EF likelihood function. On this basis, the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations. Simulation results show that the proposed method can improve the tracking performance of multiple targets, especially in heavy clutter environments.

Keywords

Multi-target tracking / Generalized labeled multi-Bernoulli / Signal features of emitter / Fuzzy C-means / Dynamic clustering

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Qiang GUO, Long TENG, Xinliang WU, Wenming SONG, Dayu HUANG. Generalized labeled multi-Bernoulli filter with signal features of unknown emitters. Front. Inform. Technol. Electron. Eng, 2022, 23(12): 1871‒1880 https://doi.org/10.1631/FITEE.2200286
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2022 Zhejiang University Press
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FITEE-1871-22010-QG_suppl_1 (684 KB)

FITEE-1871-22010-QG_suppl_2 (250 KB)

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