Aiming at problem of decline or even the divergence of precision of conventional Kalman filtering estimation when noise statistics information are unknown or time-varying

an innovation adaptive filtering algorithm based on the maximum likelihood estimation is proposed. The maximum likelihood-based conventional innovation covariance estimator is corrected by the limited memory exponential attenuation weighting to increase usage weight of recent innovation covariance sequences in sliding window. According to innovation adaptive principle

calculate filtering gain matrix directly utilizing estimated value of innovation covariance

which promotes the convergence rate of filter and improves estimation precision of filtering algorithm at the same time. Simulations are performed in the strapdown inertial navigation system/global positioning system（SINS/GPS） integrated navigation system

and the results show that the proposed algorithm has stronger robust and higher filtering precision when the noise statistics information are unknown or time-varying.