Radio Map Noise Reduction Method Using Hankel Matrix for WLAN Indoor Positioning System

Ma, Lin; Zhao, Wan Li; Xu, Yong; Li, Cheng

doi:10.1109/glocom.2017.8253939

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…Ma et al [31] proposed a radio map recovery method based on the inexact incremental Lagrange multiplier (IALM) algorithm, which accurately recovered the RSS data in the radio map and effectively reduced the noise. In addition, Ma et al [32] proposed a radio map noise reduction method using the Hankel matrix. The special structure of the Hankel matrix is used to effectively separate the noise from the signal.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Related Workmentioning

confidence: 99%