基于EKF的多机载传感器误差精确估计

Exact Estimation of Airborne Multi-sensor Bias with Extended Kalman Filter

基于固定多传感器误差估计EX方法,针对机载平台存在姿态角系统误差的问题,采用拓展卡尔曼滤波对非线性量测方程线性化近似,利用相同的随机点目标,在对目标的状态估计和误差估计之间的协方差不做近似的情况下实现它们的解耦合,提出了一种改进的多机载传感器系统误差精确估计(EEX)方法。仿真结果表明,相对于MLRM方法,EEX方法估计精度提升了近30%。通过目标状态估计得到的传感器误差伪测量的加性噪声都是零均值和方差已知的高斯白噪声,使得估计结果非常接近Cramer-Rao下界(CRLB),说明此估计方法是一个充分估计。

Based on EX algorithm for error estimation of fixed multiple sensors,and considering the Euler angle bias of the airborne sensor,we proposed an improved error estimation algorithm,named as EEX,for estimating the error of airborne multi-sensor system exactly.Extended Kalman Filer（EKF） was used to linearize the non-linear measurement equation.The same random point targets were used,the decoulpling between the target state estimation and the sensor bias estimation was achieved without approximating the cross-covariance between the state estimate and the bias estimate.Simulation results show that： compared with MLRM method,EEX algorithm improves the estimation accuracy by nearly 30%.In addition,the additive noises of pseudo-measurements of the sensor bias obtained through state estimation are all white Gaussian noise with zero-mean and known variance,thus the estimation result is very close to the CramerRao Lower Bound（CRLB）,which means that the proposed estimation is statistically efficient.