To address the issues of low filtering accuracy and poor reliability encountered when employing the Sage-Husa adaptive filtering algorithm in combined navigation systems,an improved Sage-Husa adaptive filtering algorithm is proposed.Firstly,on the basis of the Sage-Husa adaptive filtering algorithm,the statistical characteristics of noise are estimated by the weighted estimation method of exponential fading memory to improve the adaptive ability of the algorithm.Then,by the introduction of the fading factor in the strong tracking filtering,the prediction mean-square error matrix is corrected online,so that the improved algorithm has the ability to cope with the uncertainties such as systematic error interference.Simulation results demonstrate that the improved Sage-Husa algorithm exhibits greater adaptive ability.It effectively suppresses filter divergence and maintains excellent filtering performance,even when faced with model error and coarse interference.Furthermore,when applied in combined navigation systems,the algorithm showcases improved stability and positioning accuracy.
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