基于滑动窗口抗差自适应滤波的SINS/DVL组合导航算法  

SINS/DVL Integrated Navigation Based on Sliding Window Robust Adaptive Kalman Filter

作　　者：张鹭 周继航 马向东[1] 张广娜 刘旭光[1] ZHANG Lu;ZHOU Jihang;MA Xiangdong;ZHANG Guangna;LIU Xuguang

机构地区：[1]中国人民解放军63892部队,洛阳471003

出　　处：《现代导航》2023年第5期324-332,共9页Modern Navigation

摘　　要：捷联惯性导航系统(SINS)/多普勒测速仪(DVL)组合导航时,需要考虑多普勒速度信息中出现的野值和噪声特性变化对导航精度的影响。针对上述问题,提出基于滑动窗口抗差自适应滤波的SINS/DVL组合导航算法。首先建立状态变换漂移误差角系统误差模型,在速度误差方程中用重力常值项代替比力项,减少比力项引起的速度误差,提高模型的准确性。然后利用新息序列判别野值,若异常则采用滑动窗口数据修正错误新息,并使用带遗忘因子的自适应滤波在线估计量测噪声。实验结果证明,新的系统误差模型和滑动窗口抗差自适应滤波能有效减缓位置误差的累积。2 h的SINS/DVL组合导航中误差航程比优于1.05%,相比于传统卡尔曼滤波性能提升39.66%。It’s necessary to consider the impact of Doppler Velocity Log(DVL)measurement outliers and its time-varying noise characteristic on navigation accuracy in Strap-down Inertial Navigation System(SINS)/DVL integrated navigation.To address the problem,a sliding window robust adaptive Kalman filter for SINS/DVL integrated navigation is proposed.Firstly,the system error model is established based on psi angle state transformation.The force item of its velocity is replaced by constant gravity term,so that the accuracy of the model is improved by reducing the speed error caused by the force item.Then the innovation sequence is used to verified outliers.If the outliers exist,the sliding window data is used to correct the innovation sequence and adaptive Kalman filter algorithm with attenuating factor is utilized for estimate observation noise.The experimental results show that the new error model and sliding window robust adaptive Kalman filter can effectively suppression the position error divergence.The position error can be controlled in 1.05%in two hours’SINS/DVL integrated navigation.The navigation position accuracy is enhanced by 39.66%compared to Kalman filter.

关 键 词：捷联惯性导航系统 多普勒测速仪 组合导航 状态变换 滑动窗口 抗差自适应滤波 

分 类 号：TN966[电子电信—信号与信息处理]