CLC number: TN953
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-10-10
Cited: 0
Clicked: 1988
Citations: Bibtex RefMan EndNote GB/T7714
Qiang GUO, Long TENG, Xinliang WU, Wenming SONG, Dayu HUANG. Generalized labeled multi-Bernoulli filter with signal features of unknown emitters[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1871-1880.
@article{title="Generalized labeled multi-Bernoulli filter with signal features of unknown emitters",
author="Qiang GUO, Long TENG, Xinliang WU, Wenming SONG, Dayu HUANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="12",
pages="1871-1880",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200286"
}
%0 Journal Article
%T Generalized labeled multi-Bernoulli filter with signal features of unknown emitters
%A Qiang GUO
%A Long TENG
%A Xinliang WU
%A Wenming SONG
%A Dayu HUANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 12
%P 1871-1880
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200286
TY - JOUR
T1 - Generalized labeled multi-Bernoulli filter with signal features of unknown emitters
A1 - Qiang GUO
A1 - Long TENG
A1 - Xinliang WU
A1 - Wenming SONG
A1 - Dayu HUANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 12
SP - 1871
EP - 1880
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200286
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.
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