为精确估计毫米波雷达/红外成像复合系统中传感器的系统误差, 提出了一种基于无偏转换测量的精确极大似然(UCM-EML)误差估计算法.根据极坐标系下的测量噪声建立误差估计模型, 据此推导似然函数和准则函数, 采用高斯-牛顿迭代法进行准则函数的优化.仿真实验结果表明, UCM-EML算法在误差估计精度和收敛速度上都优于精确极大似然估计算法和修正的精确极大似然算法.

An exact maximum likelihood error estimation algorithm based on unbiased converted measurements (UCM-EML) was proposed in order to estimate systematic errors of a MMW radar/IR imaging composite system accurately. An error estimation model was formulated based on measurement noises in polar coordinates, then the criterion function and the corresponding negative log likelihood function were given. The algorithm was implemented using a recursive two-step optimization that involves a modified Gauss-Newton procedure. Simulation results show that the UCM-EML algorithm is better than the exact maximum likelihood (EML) algorithm and the modified exact maximum likelihood (MEML) algorithm on performance and convergence rate.