Adaptive SRCKF algorithm for near-space hypersonic target

Abstract

Near space hypersonic vehicle is characterized by its high speed and high maneuverability, so the more stable algorithm, SRCKF: Square-root Cubature Kalman Filter, was applied to track it. But in the state of target's strong maneuvering, observation noise always increases, traditional CKF algorithm easily lead tracking divergence, tracking accuracy reducing and even losing targets. Aiming at this problem, a new adaptive algorithm called AF-SRCKF: Active Function SRCKF, was put forward. Active Function was introduced to real-time correct innovation covariance matrix of SRCKF algorithm, in order to reduce the influence of observation noise caused by strong maneuvering. Simulation experiments show that compared with traditional SRCKF algorithm, in the state of large observation noise, the new algorithm could judge and restrain the filtering divergence, improve the stability of filtering and reduce the tracking error.