Application of the robust autoencoder to reduce reverberation and facilitate underwater target tracking

Reverberation is the primary interference in active underwater target tracking, increasing the difficulty of the precise location of targets. To improve the accuracy of target detection under reverberation conditions, a novel sparse track-before-detect algorithm integrating a robust autoencoder and...

Full description

Saved in:
Bibliographic Details
Published in:Applied acoustics Vol. 228; p. 110303
Main Authors: Xiang, Wenjie, Song, Zhongchang, Gao, Zhanyuan, Yang, Wuyi, Zhang, Boyu, Yang, Hongjun, Tu, Jianqiu, Li, Baoyu, Zhang, Hairui, Zhang, Yu
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.01.2025
Subjects:
Interface reverberation
Robust autoencoder
Track-before-detect based on particle filter
Underwater target tracking
Underwater target tracking
Interface reverberation
Robust autoencoder
Track-before-detect based on particle filter
ISSN:0003-682X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Reverberation is the primary interference in active underwater target tracking, increasing the difficulty of the precise location of targets. To improve the accuracy of target detection under reverberation conditions, a novel sparse track-before-detect algorithm integrating a robust autoencoder and a particle filter (PF-RAE-TBD) is proposed in this paper. This method uses the robust autoencoder to build a sparse estimation model for matched received echoes. The actual measurements are then substituted with the sparse component of the target echo constructed by the nonlinear estimation. Subsequently, the track-before-detect based on particle filter (PF-TBD) is employed to track the movement of the target. Simulation and experimental results collectively demonstrate that the proposed algorithm significantly improves the performance of the active sonar in tracking targets under reverberation conditions. Using the same dataset collected in the field, the PF-RAE-TBD algorithm improves the probability of target detection by 52.94% and 22.35% compared with the conventional PF-TBD and PF-PSO (particle swarm optimized)-TBD algorithms. The PF-RAE-TBD can provide additional contributions to improve the performance of active sonar in tracking targets under strong reverberations. •Autoencoders estimate sparse components of the echoes received by matched filter.•A sparse track-before-detect method is proposed to track the target's movement.•The method performs well with a detection probability reaching 85.73% as SRR falls to −10 dB.•The performance and accuracy of the method are validated through simulations and field experiments.
ISSN:0003-682X
DOI:10.1016/j.apacoust.2024.110303