论文部分内容阅读
为选择最佳参数估计方法估计目标微多普勒特征,需要研究参数估计的克拉美-罗界,来评价各估计方法的性能。以相干激光探测为背景,考虑噪声方差未知的影响,严格推导了高斯白噪声环境下微动目标回波信号各参数估计的克拉美-罗界的闭合表达式,仿真分析了目标相对于雷达的位置信息、数据处理长度以及回波信噪比与参数估计方差下界的关系。结果表明,克拉美-罗界与噪声方差无关,目标相对于雷达的方位角、俯仰角越小,数据长度和信噪比越大,参数估计的方差下界越小。对目前常用的两种微动参数估计方法方差进行了计算,并与推导克拉美-罗界进行了对比。最后,与通过近似处理方法得到的克拉美-罗界进行了对比,指出了精确推导方差下界的意义。
In order to choose the best parameter estimation method to estimate the target micro-Doppler characteristics, we need to study the Keramy-Lojie parameter estimation to evaluate the performance of each estimation method. Taking the coherent laser detection as the background and considering the influence of the unknown noise variance, the closed-form expression of the Cramer-Rao bound of each parameter of the fretting target echo signal under the Gaussian white noise condition is strictly deduced. The simulation analysis of the target relative to the radar Location information, data processing length and echo signal to noise ratio and the lower bound of the variance of the parameter estimation. The results show that the Kramer-Rao boundary is independent of the noise variance. The smaller the azimuth angle and pitch angle of the target relative to the radar, the larger the data length and signal-to-noise ratio, the smaller the variance bound of the parameter estimation. The variances of the two commonly used methods of inching parameter estimation are calculated and compared with the derivation of the caratme-loh bound. Finally, it is contrasted with the clarithromo-raman boundary obtained by the approximate processing method, and the meaning of accurately deriving the lower bound of the variance is pointed out.