Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China.
University of Central Florida, Orlando, FL 32836, USA.
ISA Trans. 2018 Jul;78:39-46. doi: 10.1016/j.isatra.2017.09.013. Epub 2017 Sep 29.
Maritime search and rescue (MSR) play a significant role in Safety of Life at Sea (SOLAS). However, it suffers from scenarios that the measurement information is inaccurate due to wave shadow effect when utilizing wireless Sensor Network (WSN) technology in MSR. In this paper, we develop a Novel Cooperative Localization Algorithm (NCLA) in MSR by using an enhanced particle filter method to reduce measurement errors on observation model caused by wave shadow effect. First, we take into account the mobility of nodes at sea to develop a motion model-Lagrangian model. Furthermore, we introduce both state model and observation model to constitute a system model for particle filter (PF). To address the impact of the wave shadow effect on the observation model, we develop an optimal parameter derived by Kullback-Leibler divergence (KLD) to mitigate the error. After the optimal parameter is acquired, an improved likelihood function is presented. Finally, the estimated position is acquired.
海上搜救（MSR）在海上人命安全（SOLAS）中发挥着重要作用。然而，当在 MSR 中使用无线传感器网络（WSN）技术时，由于波浪阴影效应，测量信息会不准确。在本文中，我们通过使用增强的粒子滤波方法开发了一种新的合作定位算法（NCLA），以减少由于波浪阴影效应而在观测模型中引起的测量误差。首先，我们考虑到海上节点的移动性来开发运动模型-拉格朗日模型。此外，我们引入状态模型和观测模型来构成粒子滤波器（PF）的系统模型。为了解决波浪阴影效应对观测模型的影响，我们开发了由 Kullback-Leibler 散度（KLD）导出的最优参数来减小误差。获得最优参数后，提出了改进的似然函数。最后，获得了估计位置。
