An improving sparse coding algorithm for wireless passive target positioning

Abstract With the developments of smart city, wireless passive localization (WPL) technique that detects targets without carrying any devices draws a lot of research attention. Some machine learning methods, such as sparse coding and deep learning, have been developed to formulate the WPL as a classification problem. Despite these methods have been proved to be effective in WPL, it remains hot topics to design a precise objective function that could detect the position of the targets with high accuracy and robustness. In this paper, we exploited a sparsity regularizer, named l o g -regularizer, in the classification objective function. By virtue of the distinguished ability in measuring sparsity, our proposed improved sparse coding algorithm (ISCA) with l o g -regularizer could position targets accurately with robust performance even in the challenging environments Experimental results show that our proposal achieves better results compared with five other machine learning algorithms. Even the input data is severely polluted by noise (SNR = −10 dB), the proposed method could still obtain high localization accuracy of 99.4%.