Algorithm Design and Performance Analysis of Target Localization Using Mobile Underwater Acoustic Array Networks

• Published in: IEEE transactions on vehicular technology Vol. 72; no. 2; pp. 2395 - 2406
• Main Authors: Su, Ruoyu, Gong, Zijun, Li, Cheng, Shen, Xuemin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic arrays; Acoustics; Arrays; best linear unbiased estimator; Doppler effect; Environmental monitoring; Iterative algorithms; Iterative methods; Localization; Location awareness; Marine environment; mobile underwater acoustic array network; Moving targets; Navigation safety; Nodes; Performance evaluation; Propagation delay; Satellite networks; space-air-ground-sea integrated network; Synchronization; Underwater acoustics
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2022.3211830

Space-air-ground-sea integrated network, as a promising networking paradigm for the sixth generation (6 G) communications, connects satellite networks, aerial networks, terrestrial networks, and marine networks. As a fundamental application of marine networks, efficient target localization in the ocean is significant for many marine and underwater applications, including oceanic environmental monitoring, subsea resource exploration, and navigation safety. In this paper, to bring more scalability and feasibility of underwater localization, a mobile underwater acoustic array network is utilized to locate underwater moving targets by leveraging linear frequency modulated (LFM) signals. In the mobile underwater acoustic network, one node is constantly broadcasting LFM signals. Based on the reflected signals received by other nodes, a method that jointly utilizes the propagation delay and the Doppler effect is proposed to simultaneously estimate the position and the velocity of the moving target. Specifically, a two-phase iterative algorithm with low computational complexity is designed to improve the estimation accuracy. Closed-form expressions of positioning and velocity estimation error are also presented. Performance evaluation shows that the proposed method clearly outperforms the least squares based approach. Moreover, the estimation accuracy of the proposed method can approach the Cramér-Rao low bound (CRLB) within two iterations. Decently good localization and velocity estimation error performance can be achieved even with an array network formed by a small number of nodes.