Abstract

With the development of information technology, indoor positioning technology has been rapidly evolving. Due to the advantages of high positioning accuracy, low cost, and wide coverage simultaneously, received signal strength- (RSS-) based WLAN indoor positioning technology has become one of the mainstream technologies. A radio map is the basis for the realization of the WLAN positioning system. However, by reasons of the huge workload of RSS collection, the instability of wireless signal strength, and the disappearance of signals caused by the occlusion of people and objects, the construction of a radio map is time-consuming and inefficient. In order to rapidly deploy the WLAN indoor positioning system, an improved low-rank matrix completion method is proposed to construct the radio map. Firstly, we evenly arrange a small number of reference points (RP) in the positioning area and collect RSS data on the RP to construct the radio map. Then, the low-rank matrix completion method is used to fill a small amount of data in the radio map into a complete database. The Frobenius parameter (F-parameter) is introduced into the traditional low-rank matrix completion model to control the instability of the model solution when filling the data. To solve the noise problem caused by environment and equipment, a low-rank matrix recovery algorithm is used to eliminate noise. The experimental results show that the improved algorithm achieves the expected goal.

Highlights

  • Location-based services (LBS) play an indispensable role in the information technology industry

  • The fingerprint-based WLAN indoor positioning system is divided into two phases: offline phase and online phase

  • We uniformly set a large number of reference points (RP) in the positioning environment and collect RSS values from access points (AP) on the RPs

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Summary

Introduction

Location-based services (LBS) play an indispensable role in the information technology industry. Outdoor positioning has already had very mature technologies such as Beidou and Global Positioning System (GPS), in the indoor environment, because of the interference of many factors, such as object occlusion, personnel walking, multipath effect in the process of signal propagation, and other noises, the difficulty of realizing indoor positioning technology is increased [4]. The WLAN indoor positioning system has the value of large-scale promotion and application. The fingerprint-based WLAN indoor positioning system is divided into two phases: offline phase and online phase. We uniformly set a large number of reference points (RP) in the positioning environment and collect RSS values from access points (AP) on the RPs. Each fingerprint is obtained by combining

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