Fast Construction of the Radio Map Based on the Improved Low‐Rank Matrix Completion and Recovery Method for an Indoor Positioning System

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.

Infinite Limits of Convolutional Neural Network for Urban Electromagnetic Field Exposure Reconstruction

“…Finally, several works have been done using matrix completion for radio map estimation [50], [51], avoiding the training phase with reference maps and where kernel methods are not used. For example, Wang et al [52] used matrix completion for low-ranked matrices to construct radio maps in an indoor system.…”

A Novel Weighted Fusion Based Efficient Clustering for Improved Wi-Fi Fingerprint Indoor Positioning

“…To ensure faster and accurate construction of the radio map, a hybrid method integrating the crowd-sourcing approach with some path loss model and interpolation technique is proposed in [22]. In [23], the lowrank matrix completion algorithm is used on the RSS data collected from fewer evenly arranged RPs in the positioning area to construct the radio map. A thorough investigation on how the radio map degrades due to several factors like environmental dynamics, changes in wireless infrastructure and/or indoor layout etc., is carried out by the researchers in [24].…”

Enhanced Radio Map Interpolation Methods Based on Dimensionality Reduction and Clustering

“…In view of that, it is possible to collect RSS values at only a small number of RPs and to further fill up the RSS values in the radio map in order to convert it into a complete Wi-Fi fingerprinting database using the low-rank matrix completion algorithm with the Frobenius parameter (F-parameter) integrated into it for the stability of the model solution when filling up the data. Apart from that, the low-rank matrix recovery algorithm is also used to suppress noise caused by the environment and equipment [15].…”