改进的强跟踪自适应UKF算法及其在大方位失准角对准中的应用  

Improved strong tracking adaptive UKF algorithm and its application in large azimuth misalignment

作　　者：李明[1] 柴洪洲[1] 靳凯迪 王敏[1] 宋开放 LI Ming;CHAI Hongzhou;JIN Kaidi;WANG Min;SONG Kaifang(Institute of Geospatial Information,Information Engineering University,Zhengzhou 450001,China)

机构地区：[1]信息工程大学地理空间信息学院,郑州450001

出　　处：《导航定位学报》2022年第6期165-172,共8页Journal of Navigation and Positioning

基　　金：国家自然科学基金项目(42074014)。

摘　　要：针对无迹卡尔曼滤波(UKF)易受系统模型参数失配、状态变化情况影响,导致滤波精度下降甚至发散问题,提出一种改进的强跟踪自适应无迹卡尔曼滤波(STAUKF)。将强跟踪滤波(STF)与UKF滤波结合,并引入多重渐消因子,有针对性地自动调节状态估计均方误差阵。根据新息向量构造检验门限函数,提高了滤波对有用历史信息的利用率。进一步引入简化的萨格-胡萨(Sage-Husa)滤波,自适应调节量测噪声方差,较传统Sage-Husa算法减少了计算量,提高了算法的鲁棒性。最后采用海上实测数据进行实验验证,并与UKF滤波、强跟踪UKF滤波(STUKF)比较。结果表明,该算法优势明显,有效缩短了大方位失准角误差收敛时间,提高了组合导航精度。较UKF滤波方位角收敛时间缩短了93%,东、北、天方向速度均方根误差分别降低89%、93%和82%,位置均方根误差分别降低98%、94%和97%。Aiming at the problem that the unscented Kalman filter(UKF) is easily affected by the mismatch of system model parameters and state changes, which leads to the decrease of filtering accuracy and even divergence, this paper proposes an improved strong tracking adaptive unscented Kalman filter(STAUKF). Strong tracking filter(STF) and UKF are combined, and multiple fading factors are introduced to automatically adjust the mean square error matrix of state estimation. The test threshold function is constructed according to the innovation vector, which improves the utilization rate of useful historical information by filtering. Furthermore, a simplified Sage-Husa filter is introduced to adaptively adjust the measurement noise variance, which reduces the amount of computation and improves the robustness of the algorithm compared with the traditional Sage-Husa algorithm. Finally, the experimental verification is carried out using the measured data at sea, and compared with UKF and strong tracking UKF(STUKF), the results show that the algorithm has obvious advantages, effectively shortens the convergence time of large azimuth misalignment errors, and improves the accuracy of integrated navigation. Compared with the UKF, the azimuth convergence time is shortened by 93%, the velocity root mean square error in the east, north, and up directions is reduced by 89%, 93%, and 82%, respectively, and the position root mean square error is reduced by 98%, 94%, and 97%, respectively.

关 键 词：强跟踪滤波 自适应 无迹卡尔曼滤波 多重渐消因子 组合导航 

分 类 号：P228[天文地球—大地测量学与测量工程]