Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking

To improve the low tracking precision caused by lagged filter gain or imprecise state noise when the target highly maneuvers, a modified unscented Kalman filter algorithm based on the improved filter gain and adaptive scale factor of state noise is presented. In every filter process, the estimated scale factor is used to update the state noise covariance Qk, and the improved filter gain is obtained in the filter process of unscented Kalman filter （UKF） via predicted variance Pk｜k-1, which is similar to the standard Kalman filter. Simulation results show that the proposed algorithm provides better accuracy and ability to adapt to the highly maneuvering target compared with the standard UKF.

Novel Simplex Unscented Transform and Filter

In this paper,a new simplex unscented transform（UT）based Schmidt orthogonal algorithm and a new filter method based on this transform are proposed.This filter has less computation consumption than UKF（unscented Kalman filter）,SUKF（simplex unscented Kalman filter）and EKF（extended Kalman filter）.Computer simulation shows that this filter has the same performance as UKF and SUKF,and according to the analysis of the computational requirements of EKF,UKF and SUKF,this filter has preferable practicality value.Finally,the appendix shows the efficiency for this UT.

基于AUV航迹追踪的优化UKF算法

标准的无损卡尔曼滤波算法(UKF)是一种高效的线性化航迹追踪算法,但UKF控制策略中的AUV系统采样时间间隔一般都被设置为常数,这可能会影响导航预测结果的误差精度。文章在简要阐述标准UKF算法原理的基础上,通过两种判断与反馈机制,调整UKF算法每一步的采样时间间隔t,从而实现对系统采样时间间隔的自适应变化。由此提出了基于马氏距离的优化无损卡尔曼滤波算法(MUKF)与基于灰色关联度的优化无损卡尔曼滤波算法(GUKF),以实现对AUV航行轨迹的精确预测与控制。对比两种优化后的UKF算法的仿真实验结果,进一步验证了之前所提出的假设。基于AUV航迹追踪技术的两种优化UKF算法与标准的UKF算法相比,具有更高的航迹预测误差精度和鲁棒性。