一种自适应协方差矩阵旋转变换卡尔曼滤波算法及其应用

在被动方式下对目标进行跟踪 ,由于系统的可观性较弱 ,很容易引起状态误差协方差矩阵的过早跳变而导致系统发散。为此 ,本文研究了一种新的卡尔曼滤波算法 -自适应协方差矩阵旋转变换卡尔曼滤波算法 ,并将其应用于水面目标被动跟踪。由于该算法的协方差更新采用状态滤波值计算雅可比矩阵 ,因而具有更好的一致性。仿真结果表明 ,该算法可以克服观测模型线性化误差带来的不良影响 ,改善由于观测噪声的统计特性不能精确已知而导致的滤波不稳定问题 ,具有良好的鲁棒性、快速性和精确性。

Adaptive Rotated Covariance Extended Kalman Filtering Algorithm and Its Application

Because of the low observability of passi ve target tracking which causes the premature covariance matrix collapse and solut ion divergence, a new Kalman filtering algorithm-adaptive rotate d covariance extended Kalman filter is presented in this paper. By using the smoothed state variable to calculate the Jacobian matrix and renew the covariance matrix, the algorithm has better consistency. The simulation resu lts show that it can overcome the bad effect caused by linearization of nonlinea r measurements model and the problem of the unstable caused by the unknown stati stic of measurement noise so as to greatly improve robustness,covergence rate an d accuracy of the filter. <