ID 原文 译文
20525 结合具有加性干扰的稀疏信号模型，通过对感知矩阵进行能量约束，最小化信号的后验协方差矩阵的迹，实现感知矩阵的优化设计。 Combining the sparse signal model with additive interference, the design of the sensing matrix is realized by minimizing the trace of the posterior covariance matrix and the energy constraint of sensing matrix.
20526 仿真不同信号稀疏度和重构算法时，感知矩阵优化对信号重构误差和重构时间的影响；分析信号先验信息存在偏差时，感知矩阵优化对重构效果的影响。 The effects of sensing matrix optimization on the reconstruction error and reconstruction time are simulated using difference sparse signal and reconstruction algorithms, and the effects of the sensing matrix optimization on the reconstruction effect are analyzed when there is a bias in the prior information.
20527 仿真结果表明，优化后的感知矩阵能够更好地获取稀疏信号中的重要信息，信号重构精度的均方误差减小约15～25 dB，重构时间减少约40%。 The simulation results show that the optimized sensing matrix can obtain the important information in the sparse signal, the mean square error of the signal reconstruction accuracy is reduced by about 15～25 dB, and the reconstruction time is reduced by about 40%.
20528 该文针对压缩感知多目标无源定位在无线定位环境中的字典失配问题，提出基于变分期望最大化算法的字典适配方法。 In order to solve the dictionary mismatch problem of Compressive Sensing (CS) based multi-target Device-Free Localization (DFL) under the wireless localization environments, a Variational Expectation Maximization (VEM) based dictionary refinement method is proposed.
20529 该方法首先根据鞍面模型建立无源字典，并将与定位环境相关的字典参数作为可调参数。 Firstly, this method builds thedictionary based on the saddle surface model, and models the environment-related dictionary parameters astunable parameters.
20530 然后，为目标位置向量建立两层的混合高斯先验模型以诱导其稀疏性。 Then, a two-layer hierarchical Gaussian prior model is imposed on the location vector toinduce its sparsity.
20531 最后，利用变分期望最大化算法估计隐藏变量的后验分布以及优化字典环境参数，实现多目标位置估计和字典适配。 Finally, the VEM algorithm is adopted to estimate the posteriors of hidden variables and optimize the environment-related dictionary parameter, thus the estimation of target locations and dictionary refinement can be realized jointly.
20532 仿真结果表明，相较于传统的压缩感知多目标无源定位方法，在变化的无线定位环境下，所提定位方法的性能优势尤为明显。 Compared with the conventional CS based multi-target DFL schemes, the simulation results demonstrate that the performance of the proposed algorithm is especially excellent in changing wireless localization environments.
20533 FIR陷波滤波器具有线性相位、精度高、稳定性好等诸多优势，然而当陷波性能要求较高时，通常需要较高的阶数，导致FIR陷波滤波器硬件实现复杂度大大提高。 FIR notch filter has many advantages such as linear phase, high precision and good stability.However, when the notch performance is required to be high, a higher order is usually required, resulting inincreased greatly hardware complexity of the FIR notch filter.
20534 该文基于稀疏FIR滤波器设计算法和共同子式消除的思想，提出一种低复杂度的FIR陷波滤波器设计方法。 Based on sparse FIR filter design algorithm andcommon subexpression elimination, a novel algorithm is proposed for the design of low complexity sparse FIRnotch filter.