ID 原文 译文
40176 Choi-Williams等时频分布由于受时频分辨率的约束，难以刻画多相码信号的细节特征。 The classic Choi-Williams and other time-frequency distribution methods constrained by the time-frequency resolution are difficult to characterize the details of polyphase codes.
40177 为此，本文提出了一种基于同步挤压短时傅里叶变换（Short-time Fourier transform-based synchrosqueezing transform，STFT-SST）和深度卷积网络的自动分类识别算法。 Here, we propose an automatic recognition method based on the short-time Fourier transform-based synchrosqueezing transform(STFT-SST)and deep convolutional network.
40178 在特征选取上，采用STFT-SST对多相码雷达信号进行时频分析，并提出一种频谱增强算法，用于提升低SNR下的时频特征表示，以获得高分辨率的时频特征图像； On the feature selection, the STFT-SST is used to radar signals for time-frequency analysis, and a spectrum enhancement algorithm is proposed to enhance the time-frequency features under low signal-to-noise ratio, then the high-resolution feature images are obtained.
40179 在分类网络上，设计了一个9层深度卷积网络，并引入Inception模块，提升网络对细节特征的捕获能力。 On the classification network, a nine-layer deep convolution network is designed, and the inception module is introduced to capture the signal's detailed features.
40180 仿真结果表明，当SNR为-8 dB时，该系统对5种特定多相码的平均识别率达91.8%，在低SNR下具有更好的识别性能。 The simulation results show that when the SNR is-8 dB, the average recognition rate for five polyphase codes reaches91.8%.
40181 为提高定位效率和定位精度，提出了一种基于联合分簇（Hybrid clustering，HC）和LASSO的室内定位算法。 The recognition performance of the proposed method is better at the low SNR.To improve the prediction speed and accuracy in indoor localization, a novel algorithm based on hybrid clustering and LASSO is proposed.
40182 该定位算法首先利用簇匹配实现目标粗定位，再在簇内采用LASSO算法进行二次精确定位。 Coarse localizer is taken by clustering matching and LASSO theory is used for fine localization.
40183 通过基于接收信号强度（Received signal strength，RSS）信号特性的K中心聚类方法结合基于物理位置的联合分簇，来降低粗定位阶段的簇匹配错误以避免粗大误差。 Besides the traditional received signal strength(RSS)based clustering, a coordinate-based clustering method is also used aiming at reducing the error caused by wrong cluster match.
40184 采用位置指纹RSS信号的覆盖向量的相似度作为分簇和簇匹配的准则来降低运算量。 The similarity of the RSS coverage vectors is used as the criterion of clustering and cluster matching to reduce the computational complexity.
40185 簇内定位阶段采用LASSO算法达到特征稀疏化，有利于目标节点存储空间和能耗的优化。 The algorithm of LASSO is applied to recover RSS signal from noisy measurements with reduced demand for power and memory.