ID 原文 译文
17765 针对复杂电磁环境下被动无线监测定位问题，该文提出广义相关熵的概念，推导了广义相关熵的性质，用以抑制阵列输出信号中的脉冲噪声。 To solve the problem of passive wireless monitoring and positioning in complex electromagneticenvironments, a generalized auto-correntropy for suppressing the impulsive noise in the array output signals isproposed and its properties are derived.
17766 为了实现脉冲噪声环境下相干分布源中心DOA和扩散角的联合估计，提出基于广义相关熵的DOA估计新方法，并证明了该方法的有界性。 To obtain the estimates of both central Direction Of Arrival (DOA)and angular spread for coherently distributed sources in the impulsive noise, a novel DOA estimation method based on the generalized auto-correntropy is proposed, and its boundedness is proved.
17767 为进一步提升算法的鲁棒性，推导了一种仅依赖阵列输出信号的自适应核函数。 To improve therobustness of the proposed algorithm, a new adaptive kernel function, which only depends on the array outputsignals, is also derived.
17768 仿真结果表明，该算法能够实现脉冲噪声环境下相干分布源参数的联合估计，相比已有算法，具有更高的估计精度和鲁棒性。 The simulation results show that the proposed algorithm can obtain the joint estimation for coherently distributed sources under impulsive noise environments, and has higher estimation accuracy and robustness than existing algorithms.
17769 针对脉冲噪声与同频带干扰并存时宽带信号的波达方向(DOA)估计问题，该文提出一种结合循环相关熵(CCE)与稀疏重构的算法。 To deal with wideband band Direction Of Arrival (DOA) estimation in the presence of impulsivenoise and co-channel interferences, a novel method is proposed with the help of Cyclic CorrEntropy (CCE) andsparse reconstruction.
17770 首先，分析了宽带信源的接收信号模型，并利用循环相关熵的性质构造出对脉冲噪声与同频带干扰具有抑制能力的宽带信号虚拟输出阵列。 Firstly, the received signal model of wideband sources is analyzed and a virtual arrayoutput is constructed, which shows resistance to impulsive noise and co-channel interferences via thecharacteristics of CCE.
17771 随后对该虚拟输出阵列进行稀疏表示，并通过归一化迭代硬阈值(NIHT)算法进行稀疏重构，从而估计宽带信号的波达方向。 Then, to extract the DOA of wideband signals, the virtual array output with a sparsestructure is represented and the Normalized Iterative Hard Thresholding (NIHT) is utilized to solve the sparsereconstruction problem.
17772 实验结果表明，该算法对脉冲噪声和同频带干扰具有很好的抑制作用，并且相较已有算法在估计性能方面有明显的改善。 Comprehensive simulation results demonstrate that the proposed method has efficientsuppression on impulsive noise and co-channel interference and it can improve both accuracy and efficiencythan existing methods.
17773 典型相关分析(CCA)是一种经典的多模态特征学习方法，能够从不同模态同时学习相关性最大的低维特征，然而难以发现隐藏在样本空间中的非线性流形结构。 Canonical Correlation Analysis (CCA) is a classic multi-modal feature learning method, which can learn low-dimensional features with the maximum correlation from different modalities. However, it is difficultfor CCA to find the nonlinear manifold structures hidden in the sample spaces.
17774 该文提出一种基于测地流形的多模态特征学习方法，即测地局部典型相关分析(GeoLCCA)。 This paper proposes a multi-modal feature learning method based on geodesic manifolds, namely Geodesic Locality Canonical CorrelationAnalysis (GeoLCCA).