Underwater Target Localization and Synchronization for a Distributed SIMO Sonar with an Isogradient SSP and Uncertainties in Receiver Locations
Underwater Target Localization and Synchronization for a Distributed SIMO Sonar with an Isogradient SSP and Uncertainties in Receiver Locations
复制标题
具有等梯度 SSP 和接收器位置不确定性的分布式 SIMO 声纳的水下目标定位和同步
DOI：
10.3390/s19091976
复制
发表时间：
2019-05-01
期刊：
影响因子：
3.9
通讯作者：
Song, Lei
中科院分区：
文献类型：
--
作者：
He, Chaofeng;Wang, Yiyin;Song, Lei
分布式单输入多输出声纳系统由一个声源和多个水下接收机组成。它为水下目标定位提供了一个重要的框架。然而，水下恶劣环境给水下目标定位带来了比地面目标定位更多的挑战，如水下所有接收机时钟同步困难、水声声速变化以及水下接收机位置的不确定性等。本文综合考虑声速变化、时间同步和接收机位置的不确定性，提出了分布式SIMO声纳系统水下目标定位与同步（UTLS）算法。在分布式SIMO声纳系统中，接收机呈星形拓扑结构，信息融合在中央接收机（CR）中进行。所有的接收机不是同步的，它们的位置是不确定的。此外，用深度相关声速剖面（SSP）对水下声速进行了近似模拟。我们通过数值模拟将我们提出的UTLS算法与几种基准算法进行比较来评估它。仿真结果表明了该算法的优越性。
A distributed single-input multiple-output (SIMO) sonar system is composed of a sound source and multiple underwater receivers. It provides an important framework for underwater target localization. However, underwater hostile environments bring more challenges for underwater target localization than terrestrial target localization, such as the difficulties of synchronizing all the underwater receiver clocks, the varying underwater sound speed and the uncertainties of the locations of the underwater receivers. In this paper, we take the sound speed variation, the time synchronization and the uncertainties of the receiver locations into account, and propose the underwater target localization and synchronization (UTLS) algorithm for the distributed SIMO sonar system. In the distributed SIMO sonar system, the receivers are organized in a star topology, where the information fusion is carried out in the central receiver (CR). All the receivers are not synchronized and their positions are known with uncertainties. Moreover, the underwater sound speed is approximately modeled by a depth-dependent sound speed profile (SSP). We evaluate our proposed UTLS algorithm by comparing it with several benchmark algorithms via numerical simulations. The simulation results reveal the superiority of our proposed UTLS algorithm.