ID 原文 译文
13914 在随钻电磁波测井工程中, 随着勘探深度加深, 信号呈现越来越微弱的特性, 有效提取强噪声背景下的微弱电磁波信号对于指导随钻工程勘探具有重要的意义。 In the electromagnetic wave logging engineering, as the depth of exploration deepens, the signal goes weaker and weaker. Therefore, the effective extraction of weak electromagnetic signals in the strong noise background is of great significance for guiding the exploration while drilling.
13915 传统的滤波方法仅滤除带外噪声, 带内噪声不能被很好解决, 针对此问题, 文章设计带外硬件滤波电路和带内基于最小均方算法的可变参数自适应谱线增强 (adaptive line enhancer, ALE) 算法来构造组合滤波算法。 The traditional filtering methods only filter the out-of-band noise, not the in-band noise. Therefore, this paper designs an out-of-band hardware filter circuit and in-band adaptive line enhancement (ALE) algorithm that based on the least mean square algorithm to construct the combination filtering algorithm.
13916 理论分析和仿真研究表明:该组合算法能够提高高动态、低信噪比的微弱电磁波有用信号的估计精度，有效提高信噪比和抑制工程环境噪声的能力。 Theoretical analysis and simulation show that this algorithm can improve the calculation accuracy of weak signal with high dynamic range and low signal to noise ratio (SNR) , effectively enhancing SNR and noise suppression ability.
13917 该组合算法在滤除带外噪声的基础上, 对于带内高斯白噪声抑制能力提高约10dB, 进一步解决了实际工程问题。 In addition, to filter the out-of-band noise, the in-band Gaussian white noise rejection is improved by about 10 dB in this algorithm, which helps to solve the actual engineering problem.
13918 提出一种基于卷积构型的单元平均恒虚警率 (convolution based cell averaging constant false alarm rate, CCA-CFAR) 快速检测算法。 A fast convolution based cell averaging constant false alarm rate (CCA-CFAR) detector based on convolution for target detection in synthetic aperture radar (SAR) images is proposed in this paper.
13919 该算法首先根据背景杂波分布模型计算待检测合成孔径雷达 (synthetic aperture radar, SAR) 图像统计量矩阵, 然后对单元平均恒虚警率 (cell averaging constant false alarm rate, CA-CFAR) 检测器构建卷积模型, 利用卷积运算实现对背景杂波的矩估计, 并求出详细的背景杂波分布函数, 最后根据分布函数计算出每个像素的判定阈值, 并对所有待检测像素是否为目标点进行判定。 As a first step, the statistic matrices of the SAR image are computed according to the background clutter distribution model. Then, a convolution model is built for the CA-CFAR detector to realize the moment estimation, and background clutter distribution function of all pixels can be obtained. Finally, the detect threshold for each pixel is calculated to determine whether the pixel is the target point.
13920 该检测算法复杂度低, 运算效率高, 能够快速实现SAR图像实时目标检测。 The algorithm has the advantages of low complexity and high computational efficiency, and can achieve SAR image realtime target detection.
13921 仿真实验证明了该方法的有效性和工程实用价值。 Experimental results demonstrate the effectiveness and usefulness of the proposed algorithm.
13922 基于空间时频分布 (spatial time-frequency distribution, STFD) 的多重信号分类 (multiple signal classification, MUSIC) 算法常用于非平稳信号波达方向 (direction of arrival, DOA) 估计, 其关键是选取合适的信号时频点。 The multiple signal classification (MUSIC) algorithm based on spatial time-frequency distribution (STFD) is investigated for direction-of-arrival (DOA) estimation of non-stationary signals, and its key step is to select the appropriate time-frequency points.
13923 文中针对传统时频MUSIC算法不能提取各信号时频点且在小角度间隔时估计性能不佳的问题, 以线性调频 (line frequency modulation, LFM) 信号为研究对象, 提出了基于时频点聚类的DOA估计算法。 Aiming at the problems that traditional timefrequency MUSIC (TF-MUSIC) algorithm can not extract the time-frequency points of each source and its poor performance in the case of small angle spacing, this paper proposes a novel DOA estimation algorithm for line frequency modulation (LFM) signals based on time-frequency point clustering.