摘要
针对机载无源定位存在滤波稳定性差、收敛速度慢、定位精度低等问题,提出一种观测域平方根UKF滤波算法,新算法充分利用了观测域滤波的自动解耦功能和平方根UKF良好的数值稳定性及非线性滤波能力,并通过最小偏度单形采样的UT变换减少了算法在状态域与观测域之间转换时存在的舍入误差,同时也提高了算法的运行效率。仿真结果表明新算法提高了滤波稳定性、收敛速度和定位精度。
In order to enhance robustness, increase the speed of convergence and improve the locating accuracy of the filter in the air-borne passive location, a novel measure space square root unscented Kalman filter was proposed which made full use of the auto matic deeoupling capability of measure space filtering and the good numerical stability as well as nonlinear filtering capability of square root unscented Kalman filter, and the minimal skew simplex unscented transformation was also used to improve the calculation speed and reduce the error of covariance matrix transformation between measure space and state space. Simulation results show that the new method has more stable performance, higher convergence speed and higher locating accuracy.
出处
《弹箭与制导学报》
CSCD
北大核心
2011年第1期193-196,共4页
Journal of Projectiles,Rockets,Missiles and Guidance
基金
973国家安全重大基础研究基金
国防基础科研基金资助
关键词
机载无源定位
观测域滤波
平方根UKF算法
最小偏度单形采样
airborne passive location
measure space filtering
square root unscented Kalman filter
minimal skew simplex transformation
作者简介
刘学（1983-），男，黑龙江大庆人，博士研究生，研究方向：单站无源定位。
