WiFi indoor positioning based on regularized online sequence extreme learning machine

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2020-10-01 DOI:10.1080/19479832.2020.1821100

Ye Tao, Long Zhao, Xiaorong Shen, Zhinpeng Chen, Qieqie Zhang

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引用次数: 1

Abstract

ABSTRACT WiFi positioning based on fingerprint has received widespread attention and practical applications. However, the fingerprints are susceptible to environmental changes, such as shadowing, multipath, temperature, humidity and obstacles. Due to the instability of received signal strength (RSS), it brings plenty of difficult for WiFi positioning with high accuracy. In this paper, a regularised online sequence extreme learning machine with forgetting parameters (FP-ELM) is adopted to solve the issue accordingly. Forgetting factor and regular factor are adopted in FP-ELM to cope with the time-varying nature of RSS and overcome the issue of irreversible matrix in OS-ELM. The fast running speed of the online sequence extreme learning machine (OS-ELM) is also maintained in FP-ELM. Extensive experiments are carried out in simulation and real experimental areas to explore the characteristics of FP-ELM. Moreover, the positioning results of FP-ELM are compared with the conventional algorithms (OS-ELM and KNN). The simulation and experimental results show when the regular factor is set properly, the positioning result based on FP-ELM algorithm is better than conventional algorithms Figures 1.

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基于正则化在线序列极限学习机的WiFi室内定位

基于指纹的WiFi定位已经得到了广泛的关注和实际应用。然而，指纹容易受到环境变化的影响，如阴影、多路径、温度、湿度和障碍物。由于接收信号强度(RSS)的不稳定性，给高精度的WiFi定位带来了很大的困难。本文采用带遗忘参数的正则化在线序列极限学习机(FP-ELM)来解决这一问题。FP-ELM采用了遗忘因子和规则因子，克服了OS-ELM中矩阵不可逆的问题，克服了RSS的时变特性。在FP-ELM中也保持了在线序列极限学习机(OS-ELM)的快速运行速度。为了探索FP-ELM的特性，我们在仿真和真实实验区进行了大量的实验。并将FP-ELM的定位结果与传统的OS-ELM和KNN算法进行了比较。仿真和实验结果表明，当规则因子设置适当时，基于FP-ELM算法的定位结果优于常规算法，如图1所示。

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).