ID 原文 译文
20545 通过与现有算法对比，所提结构具有较小的舍入噪声增益。 Compared with the existing algorithms, the proposed structure has a smaller roundoff noise gain.
20546 对于地杂波存在情况下的降水粒子分类问题，传统方法在不同的天气及环境条件下会产生较大分类误差。 For the problem of hydrometeor classification in the presence of ground clutter, traditional methods produce large classification errors under different weather and environmental conditions.
20547 该文提出一种基于模糊神经网络(FNN)-模糊C均值聚类(FCM)算法的双偏振气象雷达降水粒子分类方法。 A new method for theclassification of Hydrometeor based on Fuzzy Neural Network-Fuzzy C-Means (FNN-FCM) is proposed.
20548 该方法首先利用双偏振气象雷达在晴空模式下接收的地杂波数据训练FNN，自适应地计算地杂波各偏振参量隶属函数的参数， Firstly,the FNN is trained by the clutter data received by the Dual-polarization weather radar in the clear sky mode.The parameters of the membership function of each polarization parameter of the clutter are calculatedadaptively.
20549 然后利用训练得到的地杂波隶属函数对降水模式下的地杂波进行抑制， Then the ground clutter in the rainfall mode is suppressed by the ground clutter membershipfunction obtained by the training.
20550 最后采用模糊C均值聚类算法对地杂波抑制后的回波进行降水粒子分类。 Finally, FCM clustering algorithm is used to classify the Hydrometeor afterclutter suppression.
20551 对实测数据的处理结果表明，该方法能够有效地抑制地杂波并获得较为精细的降水粒子分类结果。 The processing results of the measured data show that the proposed method can effectively suppress ground clutter and obtain finer hydrometeor classification results.
20552 针对特显点选取易受噪声影响这一问题，该文提出一种基于全局图像最大对比度的逆合成孔径雷达(ISAR)方位定标算法，并在实现方位定标的同时完成距离空变相位补偿自聚焦。 Due to the selection of dominant scatterers is easy to be affected by noise, a novel Inverse SyntheticAperture Radar (ISAR) cross-range scaling algorithm based on image contrast maximization is proposed, whichcan realize the cross-range scaling while achieving the range spatial-variant phase autofocus.
20553 该方法以图像对比度作为代价函数，利用BFGS算法实现代价函数的最大化高效求解，获得目标信号的距离空变调频率，进而计算目标有效转动角速度，实现方位定标和距离空变相位自聚焦。 With the image contrast as cost function, the cross-range chirp rate of received signal can be estimated accurately using Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Based on the estimated results, the cross-range scalingof ISAR image and precise phase autofocus can be implemented.
20554 仿真和实测数据实验对比验证了该算法的有效性和稳健性。 Both simulated and real data experiments confirm the effectiveness and robustness of the proposed algorithm.