ID 原文 译文
49907 为解决该问题，该文对修正的偏置相位中心近似方法进行改进，将双根号形式距离历程表示为类收发合置项、距离空变项和方位空变项之和的形式，有效提高了距离历程的近似精度; To solve this problem, this paper modified phase center offset approximation method was improved, the double square root form distance journey represented as classes transceiver or buy items, empty variable distance and azimuth of the sum of the empty variable form, effectively improve the approximation precision of the distance course;
49908 在利用驻定相位原理求取方位向驻定相位点的过程中，通过充分考虑距离历程方位空变性的影响，推导了更为精确的方位向驻定相位点， In use standing phase principle to calculate the bearing to standing in the process of phase points, by fully considering the influence of the distance course bearing empty degeneration, more precise azimuth was deduced to standing phase points,
49909 有效提高了点目标响应二维谱的准确性。 effectively improve the accuracy of the two-dimensional point target response spectrum.
49910 在此基础上，提出一种适用于高分辨多子阵合成孔径声呐成像的距离多普勒算法， On this basis, put forward a kind of consistency condition is suitable for high resolution array synthetic aperture sonar imaging range doppler algorithm,
49911 仿真试验和实测数据成像结果验证了该算法的有效性。 simulation test and the measured data imaging results demonstrate the effectiveness of the proposed algorithm.
49912 方向图可重构天线能够根据实际需要实时改变阵列天线的方向图，稀疏天线阵在满足方向图要求的前提下可以有效降低天线设计的复杂度。 Pattern reconfigurable antenna can according to the actual need to change the direction of antenna array, real-time sparse antenna array on the premise of meet the requirements of pattern can effectively reduce the complexity of the antenna design.
49913 提出了一种基于多任务学习的方向图可重构稀疏阵列天线设计方法。 Put forward a kind of based on multitask learning pattern reconfigurable sparse array antenna design method.
49914 将稀疏阵列优化设计及其方向图综合问题转换成为稀疏矩阵的线性回归问题，利用多任务学习能同时对多个相关任务优化学习的特性，建立了多个方向图联合赋形的多任务学习模型。 Sparse array optimization design and the conversion pattern synthesis problems become sparse matrix linear regression problems, use of multitasking can be related to multiple tasks at the same time to optimize learning features, set up a multiple pattern combined with vehicle multitasking learning model.
49915 通过迭代收缩阈值的方法，对多任务学习问题进行优化求解，使得阵列天线能够使用更少的阵元实现多个方向图的重构。 Through iterative threshold shrinkage method, optimize the multitasking learning problems to solve, the array antenna arrays can use fewer implement multiple patterns of refactoring.
49916 仿真结果表明，该方法可以生成相同阵列结构的稀布天线阵，并通过动态改变其权值向量，实现多个方向图的精确赋形。 The simulation results show that this method can generate the same antenna array, the array structure of thin cloth and by dynamically changing the weight vector, implement multiple direction figure accurate informs.