Large Environment Indoor Localization Leveraging 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 networks (WLANs). However, in practice, we find that the location errors and computing complexity increase significantly as the dimensionality of the sparse vector and measurement matrix are high in a large environment, so most CS-based localization techniques are accompanied by coarse localization and access point (AP) selection stages. Therefore, in this article, we first deduced the relationship between the number of APs and the dimensionality of the sparse vector theoretically to give the guideline that the number of subdatabases and APs should be obtained. Then an adaptive intuitionistic fuzzy C-ordered mean (AIFCOM) clustering is designed for the data with outliers in the environment with multipath effects. Finally, in the fine localization stage, we propose a semi-tensor product CS (STP-CS) model to construct the measurement matrix, compared with the traditional CS model, our model not only remains more number of APs, but also decreases the dimensionality of measurement matrix, which can reduce the storage space and improve localization accuracy simultaneously.

I. Introduction

Location-based services (LBSs) [1] have been widely used in various fields, including epidemic prevention and control [2], medical monitoring [3], emergency rescue and personnel navigation [4], [5], transportation and logistics [6], personalized information transmission [7], and context awareness [8] to meet people’s diverse needs. In outdoor environments, the most widely used is the global positioning system (GPS) [9], which has high positioning accuracy and is mature enough, but the GPS needs a direct line of sight (LOS) between the satellites and the user, which is not applicable in the indoor environment. Therefore, localization techniques based on radio frequency identification (RFID) [10], [11], ultrawide band (UWB) [12], Bluetooth [13], ZigBee [14], frequency modulation (FM) [15], infrared (IR) [16], ultrasound or sound technologies [17], visible light [18], inertial systems [19], geomagnetic fields [20], and wireless local area network (WLAN) [21] methods have been proposed one after another. Fortunately, due to its low deployment cost and large signal coverage, WLAN has become one of the most common methods for indoor localization.