基于SRUKF的组网雷达系统偏差估计方法

雷达组网数据处理首先要进行误差配准，来准确地估计和消除系统偏差。该算法模型建立在ECEF坐标系下，并采用均方根不敏卡尔曼滤波（SRUKF）对组网雷达系统偏差进行实时估计。该算法不仅无需计算雅可比矩阵，而且算法的方根形式增加了数值稳定性和状态协方差的半正定性。仿真结果表明，该算法可以有效地实现组网雷达的系统误差配准。

Estimation Method of Sensor Bias Based on SRUKF for 3D Radar

Registration is a prerequisite process for the data fusion in radar networking system to esti- mate and correct systematic errors accurately. Earth-centered-earth-fixed（ECEF） coordinates is taken as the common reference coordinates, the square root unscented Kalman filter（SRUKF） is used to estimate the sen- sor bias for radar network. Moreover, the algorithm not only is unnecessary to calculate the Jacobi matrix, but also the square root forms add benefit of numerical stability and guarantee positive semi-definiteness of the state covariance. The simulation results show that the algorithm could fulfill error registration of netted radars effectively.