改进的强跟踪UKF算法及其在INS/GPS组合导航中的应用

Improved strong tracking UKF and its application in INS/GPS integrated navigation

针对标准UKF缺乏对系统状态异常的自适应调整能力,导致滤波精度降低的问题,提出一种改进的强跟踪UKF算法。该算法采用假设检验的方法对异常状态进行检测,当系统状态发生异常时,对预测协方差阵引入次优渐消因子自适应的调整滤波增益,实现对系统真实状态的强跟踪。该算法中次优渐消因子的确定无需计算系统模型的雅克比矩阵,提高了传统强跟踪UKF的实用性。将提出的算法应用于INS/GPS组合导航系统进行仿真验证,并与标准UKF进行比较,结果表明,在系统状态存在异常时,提出的带单重次优渐消因子的强跟踪UKF得到的东向、北向位置误差在[-13.7 m,14.9 m]以内,带多重次优渐消因子的强跟踪UKF得到的东向、北向位置误差在[-10.0 m,12.1 m]以内,滤波性能明显优于标准UKF,提高了组合导航系统的解算精度。

The performance of the standard UKF would be degraded when the system states are abnormal due to the absence of capability to adapt itself to the changing conditions. This paper presents an improved strong tracking UKF (ISTUKF) to overcome the limitation of the standard UKF. The hypothesis testing method is employed in the ISTUKF to identify the abnormal system states, and in case they occur, the suboptimal fading factors are introduced into the prediction covariance to adaptively adjust the Kalman gain matrix to realize the strong tracking of the real state. Compared with the traditional strong tracking UKF, the proposed ISTUKF avoids the cumbersome evaluation of Jacobian matrices involved in the calculation of the suboptimal fading factors, making it more applicable. The proposed ISTUKF is applied to the INS/GPS integrated system for simulation in comparison with the standard UKF. The simulation results demonstrate that the position errors in east and north obtained by the ISTUKF with single suboptimal fading factor are within [-13.7 m, 14.9 m], and the corresponding errors obtained by the ISTUKF with multiple suboptimal fading factors are within [-10.0 m, 12.1 m]. The performance of the proposed ISTUKF is significantly superior to that of the standard UKF, leading to improved position precision.