Study on the trajectory measurement filter algorithm for an adaptive IMM aerial vehicle

Abstract

A motion state model set for maneuvering target has been established to solve the problem of the increased trajectory measurement error in the maneuvering process of aerial vehicle. The low accuracy of filter caused by the incomplete description of motion state for the single model has been solved by utilizing the interacting multiple model (IMM) algorithm. The adaptive factor has been introduced to the Unscented Kalman Filter (UFK) to resolve the inaccurate observation error prior by adjusting the process noise covariance matrix in real time. The adaptive probability conversion factor based on compression ratio has been added to the IMM algorithm, enhancing its convergence rate. At last, a new AIMM-AUKF trajectory measurement filter algorithm has been formed so that the high accuracy measurement for the trajectory of random maneuvering target has been achieved and the validity the proposed algorithm has been verified through simulation.