DOA Tracking Algorithm Based on AVS Pseudo-Smoothing for Coherent Acoustic Targets

A direction-of-arrival (DOA) tracking algorithm based on AVS pseudo-smoothing, referred to as the FOC-M<inline-formula><tex-math notation="LaTeX">\delta</tex-math></inline-formula>-GLMBF algorithm, is proposed to track coherent acoustic targets. This algorithm adapt...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 59; no. 6; pp. 1 - 19
Main Authors: Zhang, Jun, Bao, Ming, Yang, Jianhua, Chen, Zhifei, Hou, Hong
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Acoustic measurements
Acoustic vector sensor
Acoustics
Algorithms
Coherence
Covariance matrices
Direction of arrival
Direction-of-arrival estimation
Estimation
Filtering algorithms
Filtration
generalized labeled multi-bernoulli
higher-order cumulants
Noise measurement
pseudo-smoothing
Random noise
Shortest-path problems
Signal to noise ratio
Target tracking
Tracking problem
ISSN:0018-9251, 1557-9603
Online Access:Get full text
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Summary:A direction-of-arrival (DOA) tracking algorithm based on AVS pseudo-smoothing, referred to as the FOC-M<inline-formula><tex-math notation="LaTeX">\delta</tex-math></inline-formula>-GLMBF algorithm, is proposed to track coherent acoustic targets. This algorithm adapts the marginalized <inline-formula><tex-math notation="LaTeX">\delta</tex-math></inline-formula>-generalized labeled multi-Bernoulli (M<inline-formula><tex-math notation="LaTeX">\delta</tex-math></inline-formula>-GLMB) fast filtering algorithm with the fourth-order cumulants (FOC) pseudo-smoothing. It introduces higher-order cumulants capable of suppressing Gaussian noise, and constructs the cumulant matrices and the likelihood function that can be used for AVS pseudo-smoothing. The processing enhances the signal-to-noise ratio (SNR) by suppressing measurement noise, and can accomplish decoherence when there are coherent targets. Based on the labeled random finite set (RFS), it additionally introduces the index label to distinguish different motion models as hidden states, and achieves better tracking performance through the weighted mixture of multiple models. By using the AVS hybrid signal as the measurement, the algorithm avoids measurement-to-track association maps in the filtering process, to effectively support the tracking problem when targets are close to each other or have intersecting trajectories. In addition, as a joint prediction-and-update strategy, the algorithm performs the hypothesis truncation by the K-shortest path method only once, thereby further compensating for the burden of cumulant calculation. Simulations and field experiments verify the superiority of the proposed tracking algorithm for coherent targets under low SNR.
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ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2023.3299901