Indoor Localization Based on Semi-tensor Product Compression Sensing

The sparsity of the localization problem makes the Compression Sensing (CS) theory suitable for indoor localization in Wireless Local Area Network (WLAN). However, when the target environment has a large number of Access Points (APs), online measurement takes a lot of time and increases the storage space of the AP selection matrix. To address this drawback, a Semi-tensor Product Compression Sensing (STP-CS) model is proposed, which reduces the time required to construct the AP selection matrix by constructing a low-order matrix, and enhances the real-time performance of online localization simultaneously. Experimental results show that, compared with the traditional methods, the proposed method has a great improvement in real-time performance and low overhead while ensuring localization accuracy.