ID 原文 译文
49987 针对雷达单次扫描时间小于目标转动周期的情况，提出了一种基于部分周期的扫描雷达目标微多普勒参数估计方法。 Rotation period for a single radar scanning time is less than the target, this paper proposes a scanning radar target was based on the part of the cycle doppler parameters estimation method.
49988 转动目标的微多普勒信号可以通过正弦信号的形式表示。 Turn the target of micro-doppler signal can be represented by sine signal.
49989 该方法从回波信号的时频域中提取出部分周期的微多普勒信号，利用正弦函数的性质，估计出目标转动频率和转动幅度，从而可以估计出目标转动半径。 The method from the echo signal in the time-frequency domain to extract the part of the cycle of micro-doppler signal, by using the properties of sine function, estimate the target rotate frequency and amplitude, which can estimate the target rotating radius.
49990 转动半径作为目标的固有特性，反映目标的结构信息和运动特征，为目标识别和分类提供有力依据， The intrinsic characteristics of rotating radius as the target, reflect the characteristics of the target structure information and exercise, provide powerful basis for target recognition and classification,
49991 通过仿真实验和实测数据验证了该方法的鲁棒性和有效性。 the method is verified by simulation results and the measured data of the robustness and effectiveness.
49992 针对"完全扰动"情况下压缩感知雷达(compressed sensing radar，CSR)观测矢量和感知矩阵严重失配，进而引起参数估计性能急剧下降的问题，提出了一种基于贝叶斯压缩感知(Bayesian compressed sensing，BCS)的稳健参数估计方法。 About "completely disturbance" case compressed sensing radar (compressed sensing radar, CSR) observation vector and perception matrix serious mismatch, which cause the problem of parameter estimation performance fell sharply, is put forward based on the Bayesian compressed sensing (Bayesian compressed sensing, BCS) robust parameter estimation method.
49993 首先构造"完全扰动"情况下CSR参数估计的稀疏线性模型，并从稀疏矢量的最大后验概率(maximum a posteriori，MAP)出发，推导了完全扰动矩阵服从柯西分布时的优化目标函数; "Be completely disturbance" structure first case CSR sparse linear model of parameter estimation, and from the sparse vector maximum a posteriori probability (maximum a posteriori, MAP), the perturbation matrix completely obeys the Cauchy distribution was deduced, the optimization of objective function;
49994 随后通过稀疏矢量和尺度参数的交替迭代，求得稀疏矢量的最优解。 Then by alternating iterative sparse vector and scale parameters, the optimal solution of the sparse vector is obtained.
49995 与现有重构算法及其改进算法相比，该方法能够有效改善CSR系统应对失配误差的稳健性，提高目标成功检测的概率和参数估计的精度。 Compared with the existing reconstruction algorithm and its improved algorithm, this method can effectively improve the CSR system response mismatch error robustness, improve the target detection probability of success and the accuracy of parameter estimation.
49996 计算机仿真实验验证了该方法的有效性和鲁棒性。 The computer simulation verified the effectiveness and robustness of this method.