ID 原文 译文
26655 最后的仿真算例验证了所提方法的有效性。 Finally the simulation examples verify the effectiveness of the proposed method.
26656 多输入多输出(multiple input multiple output，MIMO)稀疏布阵虽然可以获得最大的连续虚拟孔径，但在机载应用中无法进行规则的稀疏布阵。 Multiple input multiple output (multiple input multiple output, MIMO) sparse disposal while it is possible to get the maximum continuous virtual aperture, but in the application of airborne sparse disposal to rules.
26657 采用模拟退火算法，以MIMO接收端的虚拟收发联合波束为优化对象，对MIMO雷达的稀疏布阵进行优化设计。 Using simulated annealing algorithm to MIMO virtual transceiver combined beam at the receiving end as optimization object, the optimized design of MIMO radar sparse disposal.
26658 通过优化MIMO雷达的发射和接收阵元位置，可以在保持主瓣在不展宽的情况下获得更好的旁瓣水平，仿真结果验证了这一方法的有效性。 Through the optimization of the MIMO radar transmitting and receiving array element position, can in keeping the main lobe in the case of no broadening better sidelobe level, the simulation results verify the effectiveness of this method.
26659 压缩感知(compressed sensing，CS)图像重建算法是CS图像获取问题的一个研究重点。 Compressed sensing (compressed sensing, CS) image reconstruction algorithm is CS for a research focus of the problem.
26660 针对传统基于稀疏性先验的重建算法不能有效重建图像的各种结构特征，为了在测量值数量不变的情况下进一步提高图像的重建质量，在稀疏性先验的基础上，引入局部自回归模型和非局部自相似性作为图像额外的先验信息，建立了非局部正则化的CS图像重建模型，并给出了相应的数值求解算法。 For traditional reconstruction algorithm based on sparse sex prior cannot effectively various structural features of reconstruction image, in order to measure the same number of cases, to further improve the quality of image reconstruction, the sparse sex prior, on the basis of introduction of local and non-local self-similarity regression models as additional prior information image, establish the nonlocal regularization of CS image reconstruction model, and the corresponding numerical algorithm is given.
26661 此外，对于重建模型中图像的自回归参数，给出一种基于非局部相似点的估计方法。 In addition, for the reconstruction model of the autoregressive parameters of the image, give a estimation method based on the nonlocal similarities.
26662 实验结果表明，较之传统的稀疏性正则化重建算法和同类的MARX(model-based adaptive recovery of compressive sensing)算法，所提算法能获得更高的图像重建质量。 The experimental results show that compared with the traditional sparse regularization reconstruction algorithm and the same kind of MARX (model - -based adaptive recovery of compressive sensing) algorithm, the proposed algorithm can obtain a higher quality of image reconstruction.
26663 随着雷达技术的快速发展和军事应用的迫切需求，目标识别问题日益受到重视，利用高分辨率雷达一维距离像进行目标识别成为雷达和信号处理领域的一个研究热点。 With the rapid development of radar technology and the urgent need of military applications, the target recognition problem is taken seriously increasingly, the use of high resolution radar one-dimensional range as for target recognition become a research hotspot in the field of radar and signal processing.
26664 雷达目标一维距离像能够反映目标形状及结构特征，而且易于获取，迄今为止采用一维距离像进行非合作目标识别已经取得了丰富的研究成果。 Radar target one dimensional range as can reflect the characteristics of the target shape and structure, and easy to get, so far with one-dimensional distance as non-cooperative target recognition has made abundant research results.