CAJ | 학술논문

为了提高组合导航系统的滤波精度，提出一种带噪声统计估计器的自适应UKF滤波算法。该算法根据协方差匹配原理，利用UKF滤波算法的残差序列与新息序列，在线估计、调整系统过程噪声和量测噪声的统计特性，提高UKF的自适应能力，克服了标准UKF在系统噪声统计未知或不准确情况下滤波精度下降甚至发散的问题。将提出的算法应用于SINS/BDS组合导航系统进行仿真验证，并与标准UKF和抗差UKF进行比较，结果表明，提出的自适应UKF得到的水平位置误差和天向误差分别在[-6.2 m,+6.4 m]与[-9.8 m,+8.6 m]以内，滤波性能明显优于标准UKF与抗差UKF，提高了组合导航系统的解算精度。
위료제고조합도항계통적려파정도，제출일충대조성통계고계기적자괄응UKF려파산법。해산법근거협방차필배원리，이용UKF려파산법적잔차서렬여신식서렬，재선고계、조정계통과정조성화량측조성적통계특성，제고UKF적자괄응능력，극복료표준UKF재계통조성통계미지혹불준학정황하려파정도하강심지발산적문제。장제출적산법응용우SINS/BDS조합도항계통진행방진험증，병여표준UKF화항차UKF진행비교，결과표명，제출적자괄응UKF득도적수평위치오차화천향오차분별재[-6.2 m,+6.4 m]여[-9.8 m,+8.6 m]이내，려파성능명현우우표준UKF여항차UKF，제고료조합도항계통적해산정도。
This paper presents a novel adaptive UKF with noise statistic estimator for the purpose of improving the filtering accuracy of integrated navigation systems. The covariance matching technique is employed in the proposed algorithm, and the innovation and residual sequences are used to estimate and adjust the covariance matrices of the process and measurement noises online. The proposed algorithm enhances the adaptive capability of the UKF and overcomes the limitation of the standard UKF, otherwise the filtering solution will be deteriorated or even divergent as the system noise statistics are unknown or inaccurate. The proposed algorithm is applied to the SINS/BDS integrated system for simulation in comparison with the standard UKF and robust UKF. The simulation results demonstrate that the horizontal position error and vertical error obtained by the proposed adaptive UKF are within [-6.2 m, +6.4 m] and [-9.8 m, +8.6 m], respectively. The performance of the proposed algorithm is significantly superior to that of the standard UKF and robust UKF, leading to improved calculation precision of the integrated navigation system.