ID 原文 译文
23255 目前基于压缩感知的跳频信号参数估计方法大多是在高斯背景噪声下进行的研究，而在非高斯 α 稳定分布脉冲噪声环境下，已有基于高斯噪声数学模型设计的算法性能下降。 Currently, FH signal parameter estimation methods based on compressed sensing are mostly under the assumption of Gaussian noise background. In non-Gaussian α  stable distribution noise conditions, the algorithms based on Gaussian noise model suffer undesirable performance degradation.
23256 针对上述问题，该文分析了 α 稳定分布噪声的大幅值脉冲满足近似稀疏性条件，利用跳频信号与噪声之间的时域特征差异将信噪分离，实现噪声抑制。 In this paper, it is analyzed and concluded that the spike pulses of the α  stable noise approximately meet sparse conditions. By using the differences of the characteristics in the time domain, the FH signal and the noise can be easily separated, and the goal of suppressing noise can be achieved.
23257 并在压缩感知框架下，建立与跳频信号特点相匹配的 3 参数字典，采用最优匹配(Optimal Match, OM)方法对跳频信号自适应分解，获取匹配原子，基于这些时频原子包含的信息估计跳频信号的参数。 Under the framework of compressed sensing, the three-parameter dictionary is constructed based on the characteristics of FH signals, then the Optimal Match (OM) for adaptive FH signal decomposition is used to obtain the matching atoms and the FH signal parameters are estimated based on the information contained by these time frequency atoms.
23258 仿真验证表明，在 α 稳定分布噪声中，与常规的跳频信号估计方法相比，该文提出的先利用噪声稀疏性去噪，再采用最优匹配提取跳频信号参数的方法(Sparsity-OM, SOM)，能够较好地抑制脉冲噪声，获得准确的参数信息，具有良好的鲁棒特性。 Simulation results show that compared with the conventional FH signal parameter estimation methods, the proposed Sparsity-OM (SOM), which uses noise sparsity to suppress the noise and then adopts the OM algorithm, improves the estimation accuracy of FH signal parameters and it is more robust to the α stable distribution noise.
23259 针对基于压缩感知(Compressed Sensing, CS)理论的传统遥感图像融合算法未能考虑源图像信息相关性的特点，该文提出一种基于分布式压缩感知(Distributed Compressed Sensing, DCS)的遥感图像融合改进算法。 The  conventional  Compressed  Sensing  (CS)  based  remote  sensing  image  fusion  algorithm  does  not consider the correlation between the source images. In this paper, a novel Distributed CS (DCS) based remote sensing image fusion algorithm is proposed to address the correlation between the source images.
23260 通过DCS 的第 1 联合稀疏模型(Joint Sparsity Model-1, JSM-1)提取源图像低频信息的公共部分和独有部分，再利用独有特征添加(UFA)的融合规则进行融合，从而提高融合精度。 The proposed algorithm extracts the common part and the unique part of the low frequency information of the source images, in the framework of Joint Sparsity Model-1 (JSM-1). The Unique Feature Addition (UFA) rule is then used to improve the fusion performance.
23261 选取 QuickBird 卫星实测图像数据对该文方法和多个传统融合方法进行仿真实验并进行评价指标的对比，结果表明该文方法融合性能相对传统遥感图像融合方法都有不同程度的提高。 In the experiments, the QuickBird images are utilized to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the fusion performance is significantly improved using the proposed algorithm, compared with several classical fusion algorithms.
23262 针对非共线多 CCD 遥感图像匹配点的分布特点，该文提出一种基于聚类的误匹配点去除方法。 Considering the distribution characteristic of the matching points of non-collinear multiple Charge- Coupled Device (CCD) remote sensing images, a new method based on clustering to eliminate the mismatching points is proposed.
23263 首先，根据匹配点的沿轨方向偏移量曲线，获取匹配点的多维特征向量。 First, the multi-dimensionality feature vector of matching points is obtained on the basis of the disparity curve in along-track direction.
23264 然后，对匹配点集进行聚类处理，将所有点聚为一个类簇，最后根据簇半径序列曲线的变化趋势区分正确点和误匹配点。 Second, all points are clustered to one cluster. Finally, the points are marked off according to the variation trend of the semi-diameter of the cluster.