中文摘要：

针对机载雷达配准时出现的偏差跳变问题，提出一种基于广义似然比（GLR）的在线配准算法．该算法通过对配准公式的测量残差进行检验，可以自适应地估计偏差跳变量．Monte Carlo仿真实验表明，与传统的配准算法相比，在偏差发生跳变时，该算法能迅速检测到跳变发生时刻并正确估计出跳变量的大小，偏差估计值可在较短的时间内收敛到跳变后的真实值，且估计精度较高，接近CR下界．

英文摘要：

In this paper, we propose an adaptive on-line registration algorithm based on generalized likelihood ratio （GLR） for airborne radars in bias jumps environment. The algorithm tests the measurement residual of the registration formula in order to decide and estimate the jumps of bias. The Monte Carlo results show that, comparing with traditional registration algorithm, the algorithm can estimate sensor bias effectively in bias jumps environment. And the estimation errors are reduced remarkably and are close to Cramer-Rao lower bound.