A Single Observer Passive Location Algorithm Based on Backward-Smoothing Cubature Kalman Filter

Single observer passive location and tracking is a typical nonlinear filtering problem， while it may have low precision estimation and slow convergence speed for the sake of low measurement precision and great initial error etc. A new filtering algorithm——cubature Kalman filter（CKF）is applied to single observer passive location and tracking in this paper，and a backward-smoothing CKF（BSCKF） is proposed, which combines backward-smoothing with the cubature Kalman filter. In the BSCKF algorithm which is of better nonlinear approximation properties, the cubature rule based numerical integration method is directly used to calculate the mean and covariance of the nonlinear random function and backward-smoothing result is used to the recursive filtering. Computer simulation shows that the locating performance of the BSCKF is apparently better than that of EKF,UKF and CKF, which has higher convergence precision and faster convergence speed.