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首页> 外文期刊>Eurasip Journal on Wireless Communications and Networking >Indoor location method of interference source based on deep learning of spectrum fingerprint features in Smart Cyber-Physical systems
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Indoor location method of interference source based on deep learning of spectrum fingerprint features in Smart Cyber-Physical systems

机译：基于智能网络系统频谱指纹特征深度学习的干扰源的室内定位方法

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摘要

The intensity acquisition and fluctuation of the signal intensity of the interference source caused by the indoor multipath effect are very great, and there is a problem that the best eigenvalue is difficult to choose. A kind of unsupervised machine learning algorithm is proposed, which can independently identify and select the optimal eigenvalue without relying on the prior information. First, the wave signal filtering is reduced and processed by kernelized principle component analysis (KPCA) algorithm. Then, the eigenvalues are selected and the redundant features are eliminated by adaptive parameter adjustment denoising auto-encoder (APADAE) algorithm. Finally, the feature vectors are classified and identified by Softmax algorithm and the classification process are optimized by the particle swarm optimization (PSO) algorithm. Experimental results of the Smart Cyber-Physical systems show that the algorithm can indirectly improve the accuracy of the source location based on improving the classification accuracy.
机译：由室内多径效应引起的干扰源信号强度的强度采集和波动非常大，并且存在最佳特征值难以选择的问题。提出了一种无监督的机器学习算法，其可以独立地识别并选择最佳的特征值而不依赖于先前的信息。首先，通过核化原理分量分析（KPCA）算法减少和处理波信号滤波。然后，选择特征值并且通过自适应参数调整去噪自动编码器（APAASE）算法来消除冗余特征。最后，通过Softmax算法分类和识别特征向量，并且通过粒子群优化（PSO）算法优化分类过程。智能网络物理系统的实验结果表明，该算法基于提高分类精度，间接地提高源地点的准确性。

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