基于EKF算法的单站无源定位跟踪研究

Study of Single Observer Passive Location Based on Extended Kalman Filter

针对无源单站定位系统中观测方程非线性的特点,给出了一种适用于非线性的卡尔曼滤波估计改进算法——EKF算法。以方位角和方位角变化率为观测量,建立了EKF算法模型。仿真结果表明EKF算法具有收敛速度快,定位精度高的特点,可以提供较高精度的单站无源定位跟踪结果。

Kalman filter can not be used directly estimates in nonlinear single observer passive location system. Bringing forward an improved algorithm for nonlinear system is EKF algorithm. The EKF algorithm model builds based on azimuth angle and azimuth angle changing rate. Simulation results show that the EKF algorithm is applied to a nonlinear system algorithm, which has a fast convergence speed and the high positioning accuracy characteristics.