基于七维状态向量反向无迹卡尔曼滤波的弹道外推算法

针对弹道模型误差、参数估计误差以及外推距离过长导致定位精度低的问题，建立了基于七维状 态向量的反向无迹卡尔曼滤波外推算法。为精确建立状态模型，该算法将弹道系数作为状态参量，纳入滤波过程。采用无迹卡尔曼滤波算法，以提高非线性估计精度。此外，由于正向滤波外推距离长，模型误差积累大，该算法采用反向滤波处理，将雷达测得的首点作为滤波终点，通过4阶龙格-库塔方程外推炮位。仿真结果表明，该算法定位精度相较原算法提高约50%.

Extrapolation Algorithm Based on the Seven-state Inverse Unscented Kalman Filter

An “inverse” unscented Kalman filter (UKF) algorithm with seven-dimensional state vector is proposed to solve the problems of low target positioning accuracy and poor fire direction ability of firefin-der radar. A state model is established by taking the ballistic coefficient as the state parameter and incorporating it into the filtering process. The UKF algorithm is used to improve the nonlinear estimation accuracy. The model error accumulates due to the long extrapolated distance of forward filtering. In the proposed algorithm, an inverse filtering is used, the first point measured by radar is used as the end point of the filter, and the artillery position is extrapolated using the fourth-order Runge-Kutta equation. The simulation results show that the proposed algorithm can effectively improve the extrapolation accuracy of artillery locating and fire correction radar.