CAJ | 학술논문

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。
단령항자자주수하항행기(AUV)협동도항산법중，계통모형시비선성적，확전Kalman려파(EKF)시침대비선성계통적흔유영향력적려파산법，단시，EKF 산법적성능엄격의뢰우일계렬모형삼수，이저사삼수왕왕수요화비흔대적대개래포획，병차상수요인공조정。해문응용일충능자동학습Kalman려파조성협방차삼수적방법，통과방진분석，증명료해학습산법가이완전자주병차고효、준학지수출Kalman려파조성삼수，진일보제고료단령항자AUV협동도항계통적도항정도。
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.