基于扩展Kalman滤波的单领航者自主水下航行器协同导航判别式训练方法研究

Discriminative Training of Kalman Filters Based Cooperative Navigation for Multiple Autonomous Underwater Vehicles with a Single Leader

单领航者自主水下航行器(AUV)协同导航算法中,系统模型是非线性的,扩展Kalman滤波(EKF)是针对非线性系统的很有影响力的滤波算法,但是,EKF算法的性能严格依赖于一系列模型参数,而这些参数往往需要花费很大的代价来捕获,并且常需要人工调整。该文应用一种能自动学习Kalman滤波噪声协方差参数的方法,通过仿真分析,证明了该学习算法可以完全自主并且高效、准确地输出Kalman滤波噪声参数,进一步提高了单领航者AUV协同导航系统的导航精度。

In the cooperative navigation algorithm for multiple Autonomous Underwater Vehicles （AUVs）with a single leader, the model of the systemis nonlinear. The Extended Kalman Filter （EKF）, which is directed against the nonlinear system, is one of the most influential techniques. However, the performance of EKF critically depends on a large number of modeling parameters which can be very difficult to obtain, and are often set by manual tweaking and at a great cost. In this paper, a method for automatically learning the noise covariance of a Kalman filter is applied, and the simulation result shows that this algorithm fully automatically and quickly outputs the noise covariance, which improves the navigation accuracy of the cooperative navigation system.