基于改进的平方根中心差分卡尔曼滤波的单站无源定位算法

Improved central difference Kalman filter for single observer passive location

针对单站无源定位可观测性弱、收敛速度慢、定位精度差等问题,推导出了一种带次优渐消因子的平方根中心差分卡尔曼滤波算法。在正交原理的约束下,通过引入自适应次优渐消因子实时调整增益矩阵,保证不同时刻残差序列相互正交,提高了滤波器对状态变化的反应速度和对有偏估计的自适应修正能力。同时,使用误差协方差的平方根替代协方差参与滤波,保证滤波算法的数值稳定性。仿真结果表明,新算法稳定性更好、收敛速度更快、定位精度更高。

A novel Suboptimal Fading Square Root Central Difference Kalman Filter（SFSRCDKF） algorithm is presented.This algorithm can enhance the observability,increase the convergence speed and improve the locating accuracy in the Single Observer Passive Location（SOPL）.It uses adaptive suboptimal fading factor restricted by the orthogonality principle to real-time adjust the gain matrix,which can ensure the orthogonality of the new observation residuals at different time.Therefore,the response speed and the adaptive correction capability of the filter are improved when the state changes or the estimation is biased.The new filter also uses square root of the error covariance instead of the error covariance involved in filtering to ensure numerical stability.Simulation results show that,under different conditions,the new algorithm performs more stably with higher convergence speed and higher locating accuracy.