Application of Adaptive Federated Kalman Filter Algorithm to Multi-parameter Estimation for Torpedo Navigation and Positioning

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.