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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.
Aiming at the problem of deviation jump in airborne radar registration, an on-line registration algorithm based on GLR (Generalized Likelihood Ratio) is proposed, which can adaptively estimate the deviation by checking the measurement residual of the registration formula Jump variable.Monte Carlo simulation experiments show that compared with the traditional registration algorithm, when the deviation jumps, the algorithm can quickly detect the moment of jump and correctly estimate the size of the jump, the deviation can be estimated in more than Convergence to a short time after the transition to the true value, and the estimation accuracy is high, close to the CR lower bound.