基于奇异值分解的改进机载单站无源定位算法

摘要

In order to solve the problems of poor filtering stability,slow convergence speed and low locating accuracy of the filtering algorithm in the single airborne observer passive location, a square root sigrna point Kal-man filtering algorithm based on singular value decomposition (SVD-SRSPKF) was proposed in this paper. The Cholesky decomposition or update was replaced by singular value decomposition, and the square root of covari-ance was used to d the filter, which ensured filtering algorithms numerical stability in the new algorithm. The simulation results showed that the SVD-SRSPKF algorithm had higher convergence speed,location accuracy and numerical stability than any other similar algorithm.%针对机载单站无源定位系统中的滤波算法存在滤波稳定性差、收敛速度慢、定位精度差等问题,提出一种基于奇异值分解的平方根sigma点卡尔曼滤波算法(Square Root Sigma Point Kalman filter based on Singular Value Decomposition,SVD-SRSPKF).新算法利用奇异值分解代替Cholesky分解或更新,并使用误差协方差的平方根替代协方差进行滤波,保证滤波算法的数值稳定性.仿真结果表明:SVD-SRSPKF算法比其他同类算法具有更高的收敛速度、定位精度和数值稳定性.