基于推广Kalman滤波的机载无源定位改进算法

Improvement on Passive Location Algorithm Based on Extended Kalman Filter

研究空中运动观测平台对地面辐射源目标的纯方位信息定位算法 ,提出改进的二阶 EKF定位算法以提高定位估计精度 .用推广 Kalman滤波算法代替传统的最小二乘定位算法 .充分利用观测平台的运动信息建立了可观测的观测方程 ,并采用二阶 EKF算法解决了在观测误差较大的情况下导致的非线性误差较大的问题 .采用 MonteCarlo仿真比较 L S,EKF和二阶 EKF3种方法的性能 .证明用这种方法可以达到更好的估计精度 ,能够将目标位置定位在更小的概率椭圆内 .概率误差椭圆缩小了 30 % .

Brings forward an improved bearing only location algorithm for airborne observers. Extended Kalman filter (EKF) is used to substitute the traditional least square location. Information about movement of the airborne platform is introduced into the observation equation to build up an observable system. Second order EKF algorithm is used to resolve the nonlinearity error produced by the nonlinearity observation function. The three methods of location are compared by Monte Carlo simulation. The results proved that second order EKF used in passive location problems led to better estimation precision. The estimation results appear in a smaller probability ellipse, which is reduced to 30% of the ellipse by LS estimation.