ID 原文 译文
53547 理论分析和仿真实验，验证了本文方法具有较高的精度、较好的分辨力、对相干信号也具有优越的适应能力，性能优于 MUSIC 算法。 The theoretical analysis and experimental results show this new method has a better performance than the MUSIC algorithm in the aspects of accuracy, resolution and adaptability to coherent signals.
53548 冗余字典的信号稀疏分解是一种新的信号表示理论，采用超完备的冗余函数系统代替传统的正交基函数，为信号自适应地稀疏扩展提供了极大的灵活性。 Signal sparse decomposition of redundant dictionaries is a new theory for signal representation. The theory can adaptively provide a flexible method for signal sparsity extension via using overcomplete redundant function instead of conventional orthonormal-basis function.
53549 本文研究了压缩感知理论下的冗余字典、测量矩阵及其限制等容特性（RIP，Restricted Isometry Property），并给出了RIP、字典大小、稀疏度和测量次数的关系，提出了一种新的迭代软阈值（IST）算法， Based on investigation for redundant dictionaries and measurement matrix as well as restricted isometry property for compressed sensing, the paper presents a novel algorithm of iterative soft threshold (IST).
53550 与正交匹配追踪（OMP）算法和迭代硬阈值（IHT）算法相比较，实验结果表明了IST算法具有更高的信号恢复率。 Comparing the proposed IST with orthogonal matching pursuits (OMP) and iterative hard threshold (IHT) respectively, experiment results show that the IST algorithm has higher signal recovery ratio.
53551 本文提出了一种基于选择性集成径向基函数神经网络（SE-RBFNN）的来波方向（DOA）估计方法，解决单个神经网络建模进行DOA估计精度低的问题。 A novel DOA estimation algorithm based on the equal-width-voting selective RBF neural network ensemble is proposed, which overcomes the low estimation precision and weak generalization ability flaws in the traditional single neural network estimation algorithm.
53552 首先利用Bagging方法训练生成一定数量的RBFNN弱分类器，其估计精度低但泛化能力强; Bagging algorithm is used to train amount individual RBFNN, which has low estimation precision but good generalization performance.
53553 然后提出并运用等宽分箱—投票选择性集成方法剔除估计误差大的奇异值个体，优选部分RBFNN输出结果进行平均处理，从而获得了高精度的DOA估计。 Then the equal-width-voting selective ensemble algorithm is proposed to extract the appropriate number of ensemble members from the available individual networks, meanwhile the DOA estimation strong learner that has higher estimation precision and better generalization ability is constructed.
53554 仿真结果表明了算法的有效性，相对单个RBFNN建模，构建的选择性集成模型能适应方向特征的变化，算法的来波估计精度显著提高。 Experimental results showthat, compared with the single RBFNN estimation algorithm, the estimation precision and generalization ability is largely improved.
53555 针对复杂体制雷达辐射源识别，提出一种基于时频分布Rényi熵的雷达信号特征提取和识别方法。 To correctly classify advanced radar emitter signals, a novel approach adopting Rényi Entropy of time frequency distribution for radar emitter signal recognition is proposed.
53556 该方法首先对雷达辐射源信号进行时频变换， Time-frequency distribution of radar emitter signals are obtained by using time-frequency reassignment transform,