A Single Observer Passive Tracking Algorithm Based on Strong Tracking Cubature Kalman Filter

System model and filtering algorithm are the core problem to maneuvering target single observer passive location and tracking.Truncation Gaussian probability model and a new filtering algorithm-cubature Kalman filter( CKF) are applied to maneuvering target single observer passive location and tracking in this paper.With reference to the strong tracking filter( STF),a strong tracking cubature Kalman filter( STCKF) is proposed by introducing a time-varying fading factor to filtering process for the sudden maneuver case.The cubature rule based numerical integration method is directly used to calculate the mean and covariance of the nonlinear random function in this algorithm and the implementation of the method is simple and higher accuracy of state estimate is achieved.By adjusting the gain matrix on-line with the fading factor,this algorithm improves the adaptive tracking performance when there is a sudden maneuver.Combining with the spatial-frequency domain model,simulation results show that,when there is only common maneuver the performance of STCKF and CKF are nearly same and better than EKF.When there is a sudden maneuver,the performance of STCKF is much better than EKF and CKF.