ID 原文 译文
24485 同时在高层采用三维分布估计算法 (three-Dimensional Estimation of Distribution Algorithm, 3DEDA) 学习和积累优质高层个体中块结构及其位置信息, 再通过采样 3DEDA 中的概率模型生成新的高层个体或排列; At the same time, the three-dimensional estimation of distribution algorithm (3DEDA) is used in the high layer to learn and accumulate the information of block structures and their positions in the high-quality high-layer individuals, and then new low-layer individuals or permutations are generated by sampling the probability model in 3DEDA.
24486 进而在低层将高层所生成的每个新个体所表征的一系列有序的启发式操作作为一种新的启发式算法, 对问题解空间执行较深入的邻域搜索。 Furthermore, in the low layer, an ordered series of heuristic operations presenting by each new individual generated in the high layer is regarded as a new heuristic algorithm to execute further neighborhood search in the problem's solution space.
24487 最后, 通过仿真实验与算法对比验证HH3DEDA可有效求解DPFVTISP。 Finally, simulations and comparisons demonstrate that HH3DEDA can effectively solve the DPFVTISP.
24488 针对冲击噪声下多输入多输出(Multiple-Input Multiple-Output, MIMO)雷达阵列诊断失效问题，对基于二阶矩的传统匹配滤波器进行改进以适应非高斯噪声，并提出一种基于张量分解和 K-means聚类的阵列诊断方法。 The traditional array diagnosis methods for multiple-input multiple-output(MIMO) radar may fail in the presence of impulse noise. The traditional matched filter based on second-order statistics is modified to obtain a reliable performance in the non-Gaussian noise, and then the array diagnosis method based on tensor decomposition and K-means clustering is proposed.
24489 该方法利用 MIMO雷达各接收阵元回波信号的高斯核函数值来自适应地调整匹配滤波器的系数，以有效形成虚拟阵列。 The coefficients of the matched filters are adjusted with the Gaussian kernel function values of the echo signal observed at each receive element, which makes the MIMO radar form a virtual array successfully in the presence of impulsive noise.
24490 为挖掘正常和故障阵元的匹配滤波输出数据的多维特征，将虚拟阵列协方差矩阵构建成三阶平行因子(PARAllel FAC‐tor, PARAFAC)张量，并通过 COMFAC(COMplex parallel FACtor analysis)算法分解获得收发阵列流形矩阵，使用欧式距离度量其相似性，确定两个簇类数据的聚类中心并划分出异常簇类，以完成故障阵元位置的诊断。 To further utilize the inherent multidimensional structure of the matched filter output data of the damaged and normal antennas, a third-order parallel factor (PARAFAC) model of the virtual array covariance matrix is formulated. By exploiting the complex parallel factor analysis (COMFAC) algorithm on the third-order covariance tensor, the manifold matrices of the transmit and receive arrays are obtained. The similarity of manifold matrix data is measured using Euclide and distance, and the clustering centers of the two clusters corresponding to the normal and fault elements are determined. The abnormal cluster data is selected to diagnose the location of the fault elements in MIMO radar array.
24491 仿真结果验证了所提算法的有效性。 Numerical simulation results confirm the effectiveness of the proposed algorithm.
24492 传统的信源信道编码调制，都是基于四十年代香农分离理论的比特级和定长符号级解调译码，采用均匀分布和等概率的星座映射，在资源受限的无线通信系统中，难以与动态变化的多径信道相匹配，不可逾越通信技术发展所面对的可靠性墙和功耗墙等障碍。 Conventional joint source-channel coded modulation is based on bit-level or fix-length symbol-level demodulation and decoding, derived from Shannon's separation theory in 1940s. Uniform distribution and equiprobable constellation mapping are used, but they are not suitable for dynamic multipath channels. Meanwhile, the assumption of separation theory cannot be satisfied in source limited communication networks, and obstacles such as reliability and power consumption cannot be surmounted faced by the development of communication technology.
24493 本文针对比特级和定长符号级调制/解调译码框架的局限，将变长信源信道编码与非等概率、优化的非均匀 APSK(Amplitude Phase Shift Keying)调制星座映射相结合，提出了不等概率可变长符号联合信源信道编码的调制方法。 The limitation of bit-level and fix-length symbol-level modulation/demodulation and decoding is paid attention. By combining variable-length joint source-channel coding with non-equiprobable and optimized unequal APSK (Amplitude Phase Shift Keying) modulation constellation mapping, a method of joint source-channel coding modulation for non-equiprobable variable-length symbols is proposed.
24494 仿真实验结果表明：该方法在 10的-5次方量级的符号差错率下，同现存的符号级 2D-8PSK 方法比较，至少可获得约 2.5dB的功率信噪比增益，同现存的比特级 16APSK 方法比较，至少可获得约 1。1dB的功率信噪比增益。 The simulation experiments show that the proposed method obtains an SNR (Signal Noise Ratio) gain over 2.5dB compared to the existing symbol-level 2D-8PSK method, and an SNR (Signal Noise Ratio) gain over 1.1dB compared to existing bit-level 16APSK method, under the SER (Symbol Error Rate) of the order of -5 of 10.