An Adaptive Cubature Kalman Filter for Target Tracking

The target tracking problem is an essential component of many engineering applications.The extended Kalman filter (EKF) is one of the most well-known suboptimal filter to solve target tracking. However, since EKF uses the first-order terms of the Taylor series nonlinear extension functions, it often makes large errors in the estimates of state. As a result, target tracking based on EKF may diverge. In this manuscript, an adaptive square root cubature Kalman filter (ASRCKF) is poposed to solve the maneuvering target tracking problem. In the proposed method, the covariance of process and measurement noises is estimated adaptively. Thus, the performance of proposed method does not depend on the noise statistics and its performance is robust with unknown prior knowledge of the noise statistics. Morover, it has a consistently improved numerical stability why the matrices of covariance are guaranteed to remain semi- positive. The performance of the proposed method is compared with EKF, and the unscented Kalman filter (UKF) for target tracking problem. To evaluate the proposed method, many experiments is performed. The proposed method is evaluated on the non-maneuvering and maneuvering target tracking. The results show that the proposed method has lower estimation errors with faster convergence rate than other methods. The proposed method can track the tates of moving target effectively and improve the accuracy of the system.