LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network

Jingqiu Ren,Ke Bao,Guanghua Zhang,Weidang Lu,Li Chu

doi:10.1177/1550147720907831

Abstract

In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.

Highlights

With the rapid development of indoor positioning technology, the demand for indoor positioning information service is increasing gradually, and location services are a growing concern
The localization algorithm is designed for localization through received signal strength indication (RSSI) of the reference node obtained from the localization node
In order to reduce the error of indoor positioning, improve the operation speed, and improve the global search ability of the algorithm, this article adopts a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function (DBSCAN-GA-RBF) neural network

Summary

Introduction

With the rapid development of indoor positioning technology, the demand for indoor positioning information service is increasing gradually, and location services are a growing concern. Without any additional hardware equipment, Zigbee network is used to provide indoor positioning services.[13] Ultrasonic positioning technology calculates the distance according to the measured ultrasonic signal, converts the distance into coordinates, and obtains the final positioning result.[14] Ultra wide bandwidth (UWB) is a wireless carrier technology based on narrow pulse, which has superior obstacle crossing ability and has been widely applied due to its advantages of high efficiency, high accuracy, low power consumption, and low price.[15] RFID system can receive data without direct contact with objects It uses the radio frequency electromagnetic field to transmit data from the RFID tag to the reader, tracking and collecting specific digital information, so as to automatically identify the target. The RBF neural network optimized by GA is used to train a small number of sample values, which can well meet the requirements of indoor positioning and reduce indoor positioning errors

Methodology

Experiments and analysis

Findings

Conclusion