基于GPS/INS的自适应无迹Kalman滤波算法

Adaptive traceless Kalman filtering algorithm based on GPS/INS

载体的姿态参数是导航系统重要的影响因素,为提高姿态角测量精度,以INS和GPS紧组合导航系统为研究背景,针对无迹Kalman滤波算法对误差模型较敏感、新息噪声干扰数据需预处理、算法实效性低等缺点,提出改进的自适应无迹Kalman滤波算法。首先,用自适应窗口在线估计系统噪声和量测噪声的协方差值,得到与实际噪声更贴近的统计特性,减小数据预处理的干扰;其次,对状态预测方差阵引入次优渐消因子减少计算量,同时为了减少模型的精度损耗,对滤波过程引入统计量,确定模型不确定性检测阈值;最后,用扩展Kalman滤波、无迹Kalman滤波和改进后的新滤波算法对无人机航向轨迹进行数据处理。结果分析可得,改进的滤波融合算法能将姿态的测量精度提高到0.1°,具有更强的收敛性,能较好地抑制漂移误差。

The attitude information of the carrier is one of the most important information in the navigation parameters.In order to improve the measurement accuracy of the attitude angle, taking INS and GPS tight integrated navigation system as the background frame, in view of the traceless Kalman filtering algorithm is more sensitive to the error model, the new information noise interference data preprocessing and the low effectiveness of the algorithm, an improved adaptive traceless Kalman filtering algorithm was proposed.The covariance difference between system noise and measurement noise was estimated online by adaptive window, and the statistical characteristics closer to the actual noise were obtained to reduce the interference of data preprocessing;For the variance matrix of state prediction, the suboptimal fading factor was introduced to reduce the calculation amount;In order to reduce the precision loss when the model was determined, the statistics were introduced to the filtering process to determine the detection threshold of model uncertainty.Finally, extended Kalman filtering, traceless Kalman filtering and improved new filtering algorithm were used to process the data of UAV’s heading trajectory.The results showed that the improved filtering fusion algorithm could improve the attitude measurement accuracy to 0.1 °.