Asynchronous Localization of Underwater Target Using Consensus-Based Unscented Kalman Filtering
Asynchronous Localization of Underwater Target Using Consensus-Based Unscented Kalman Filtering
复制标题
基于离散余弦的UKF水下目标异步定位
DOI：
10.1109/joe.2019.2923826
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发表时间：
2020-10
影响因子：
4.1
通讯作者：
Jing Yan;Haiyan Zhao;Xiaoyuan Luo;Yiyin Wang;Cailian Chen;X. Guan
中科院分区：
文献类型：
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作者：
Jing Yan;Haiyan Zhao;Xiaoyuan Luo;Yiyin Wang;Cailian Chen;X. Guan
大多数水声传感器网络的应用都依赖于精确的目标位置信息。然而，与陆地传感器网络相比，水下环境的异步时钟、分层效应和强噪声特性使目标定位更具挑战性。本文研究了在等梯度声速和噪声条件下水下目标的异步定位问题。首先设计一个包括水面浮标、传感器和目标的网络架构，其中传感器和目标上的时钟不需要同步。为了消除异步时钟的影响，我们建立了传播延迟与位置的关系。特别地，采用射线追踪方法来模拟分层效应。然后，提出一个定位优化问题，使所有测量误差之和最小。为了解决定位优化问题，提出了一种基于共识的无气味卡尔曼滤波（UKF）定位算法，给出了算法的收敛条件和cram<s:1> rs - rao下界。最后，仿真结果表明，与穷举搜索方法相比，所提出的定位方法可以减少定位时间。同时，基于共识的UKF定位算法与其他工作相比，可以提高定位精度。
Most applications of underwater acoustic sensor networks (UASNs) rely on accurate location information of targets. However, the asynchronous clock, stratification effect, and strong-noise characteristics of underwater environment make target localization more challenging as compared with terrestrial sensor networks. This paper focuses on an asynchronous localization issue for underwater targets, subjected to the isogradient sound speed and noise measurements. A network architecture including surface buoys, sensors, and the target is first designed, where the clocks on sensors and the target are not required to be synchronized. To eliminate the effect of asynchronous clocks, we establish the relationship between the propagation delay and the position. Particularly, the ray tracing approach is adopted to model the stratification effect. Then, a localization optimization problem is formulated to minimize the sum of all measurement errors. To solve the localization optimization problem, a consensus-based unscented Kalman filtering (UKF) localization algorithm is proposed, where the convergence conditions and Cramér–Rao lower bounds are also given. Finally, simulation results reveal that the proposed localization approach can reduce the localization time by comparing with the exhaustive search method. Meanwhile, the consensus-based UKF localization algorithm can improve localization accuracy as compared with other works.