自适应联邦滤波算法在鱼雷多参量导航定位中的应用

Application of Adaptive Federated Kalman Filter Algorithm to Multiparameter Estimation for Torpedo Navigation and Positioning

针对鱼雷水下航行的特殊性以及组合导航系统中由于量测噪声统计特性的不确定而导致滤波精度降低的问题,提出了一种新的应用于鱼雷导航定位的自适应滤波算法。该算法通过新息自适应量测噪声,在噪声统计特性未知的情况下能进行滤波计算。同时在信息融合时提出一种新的自适应信息分配方法,该方法利用估计的均方误差阵来实时确定各子滤波器的信息分配系数,使信息分配系数能够跟随子滤波器的性能而改变。通过对新算法与标准卡尔曼滤波算法所做的对比仿真试验分析,结果表明,该自适应联邦滤波算法在鱼雷多参量自适应联邦滤波导航定位应用中的有效性。

Considering the particularity of torpedo underwater navigation, a new adaptive Kalman filter algorithm for torpedo multi-parameter estimation is presented for the purpose of torpedo navigation and positioning. Hence, the low filtering precision due to the uncertainty of the measurement noise＇s statistical characteristics of Kalman filter in inte- grated navigation can be improved. This algorithm can conduct filtering calculation via measurement noise＇s adaptive information with unknown statistical characteristics of noise. Moreover, a new adaptive information distribution strategy for information fusion is proposed. This distribution strategy can determine the information distribution coefficient of each sub-filter by making use of the estimated mean square error matrix, and make the variation of the coefficient de- pend on the optimal performance of the sub-filter at any time. Comparison between the simulations of the proposed al- gorithm and the normal Kalman filter algorithm verifies the effectiveness of the proposed algorithm.