Kalman Filtering for Ultra-Tight Coupled from Stable State to Catastrophe State in Noise Condition

In noise condition, high dynamic aerocraft change from stable state to catastrophe state will lead reduction of performance of Kalman filter, and then reduce reliability of GPS/INS integration, which influence anti-jamming of navigation system and reduce accuracy of navigation integration. In order to improve Kalman filter's performance, enhance performance of anti-jamming of navigation system and increase accuracy of navigation integration, a new fading interval Quadrature Kalman filtering algorithm was proposed in this paper. The algorithm could adjust its filtering gain matrix on line and interval operation can not only ensure the completeness of set mapping but also can be the optimization by introducing a time-varying fading factor and interval matrix. The simulation performance statistics show that the new Kalman filtering algorithm can overcome limitation of QKF algorithm, which can improve performance of tracking. Complexity of algorithm was not increased acutely.