基于AUV的航迹追踪自适应UKF算法

无迹卡尔曼滤波算法(UKF,unscented kalman filter)是一种常见的(AUV,autonomous underwater vehicle)加权统计线性回归航迹追踪算法,其算法冗余度低于(EKF,extended kalman filter)、(PF,particle filter)及(PSO,particle swarm optimization)等数值优化算法,且算法效率较高。然而,UKF控制算法中的系统采样时间间隔通常会被设置为常数,由此可能会产生预测值的误差累积,从而影响导航预测结果的精度。因此,笔者提出了基于AUV的航迹追踪自适应无迹卡尔曼滤波算法(AUKF,adaptive unscented kalamn filter algorithm),以期降低预测算法的累积误差。该预测方法依据标准UKF算法的原理,通过构造相应的约束、判断与反馈机制,调整系统状态方程中每一步的采样间隔t,从而提升算法的航迹追踪精度并减少过程噪声及传感器噪声对预测过程的影响。最后,通过仿真实验与结果对比,近一步验证了之前所提出的设想。

Adaptive UKF algorithm based on AUV tracking

The unscented Kalman filter (UKF) is a common weighted statistical linear regression AUV tracking algorithm. Its computational redundancy is lower than those of numerical optimization algorithms such as EKF,PF and PSO, and the tracking efficiency is high. However, the systematic sampling interval in the UKF control method is usually set to a constant value, which may result in the accumulation of errors in the predicted values and influence the accuracy of the navigation prediction results. Therefore, an adaptive unscented Kalman filter algorithm (AUKF) based on AUV tracking is proposed in this paper to reduce the cumulative error of the predicted algorithm. Based on the standard UKF algorithm principle, this prediction method adjusts the sampling interval t of each step in the system state function through the construction of corresponding constraint,judgment and feedback mechanism, so as to improve the tracking accuracy of the algorithm and reduce the influence of the process noise and sensor noise on the prediction process. By simulation experiment and the result comparison, the idea put forward earlier is verified.