Contents I. Introduction
With the development of Internet of Things (IoT) technology, Location-Based Service (LBS)[1] has been used in various fields, in which positioning technology is one of the most important key technologies. Due to the pervasiveness of Wireless Local Area Network (WLAN), indoor localization systems based on WLAN have become one of the most common methods for indoor environment. Generally, there are two types of indoor localization techniques, one is based on signal spatial similarity and the other is based on sparse recovery technique, where the Compressive Sensing (CS) is mostly utilized. Yan[2] designed a localization scheme to enforce the Restricted Isometry Property (RIP) and maximize the signal-to-noise ratio (SNR) by introducing optimization of the measurement matrix. Khan[3] proposed a method to estimates the location of unknown/target nodes using Bayesian CS. However, when the target environment deploys a large number of Access Points (APs), the AP selection matrix constructed by the traditional CS methods[4] will increase computational complexity and storage space. Hence, this paper proposes a novel Semi-tensor Product Compression Sensing (STP-CS) model, which satisfies the two criteria of RIP and Mutual Incoherence, and can obtain an equal localization performance as traditional CS but reduces the storage space and computational complexity by constructing a low-order random measurement matrix, and realizes real-time and accurate location estimation.
