Adaptive unscented Kalman particle filtering method

The invention discloses an adaptive unscented Kalman particle filtering method. The method utilizes the theory of the Sage filtering windowing method, also combines the idea of fading, estimates a true covariance matrix of the observed quantity by collecting an epoch innovation vector, and compares the true covariance matrix with the covariance matrix of a filtering recursive model, when a deviation exists between the two covariances, the observed covariance matrix of the system is adaptively adjusted according to the difference. Based on the process, an adaptive fading factor is designed, theobservation noise is further modified, the modified observation noise participates in the solution of a gain matrix, and thus the state estimation can be adaptively adjusted. The scheme of the invention can effectively perform filtering correction on a strongly nonlinear satellite/inertial integrated navigation system, especially when the external noise is abnormal, the filtering gain can be effectively and adaptively ad