Weighted Multiple Linear Regression Model for Mobile Location Estimation in GSM Network

Weighted Multiple Linear Regression Model for Mobile Location Estimation in GSM Network

Longinus Sunday Ezema, Cosmas I. Ani
DOI: 10.4018/IJITN.2020010105
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The numbers of crimes and accidents, among other challenging issues, requiring a mobile application with localization capabilities are on the increase. Yet there is under-utilization of location information provided by mobile phones. The accuracy and cost of implementation of mobile position localization on cellular network have been an issue of research interest. In this paper, the statistical modelling of mobile station (MS) position location was carried out using weighted multiple linear regressions (WMLR) method. The proposed statistical modelling approach was based on received signal strength (RSS) technique. The model improved localization accuracy. The model's simulated results were analysed and compared with the existing MLR using real measured data collected from GSM network in a light urban environment in Enugu, Nigeria.
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In the field of mobile application, location base services have been an interesting topic for researchers. The following research works in mobile location in GSM network have been reported in journal publication and reviewed in this section.

Mobile location estimation using an interpolative neural network was proposed in (Chien-Sheng, Jium-Ming, and Chin-Tan, 2013). It used three angles of arrival measurement to achieve a better result than the analytical (the weighted average and optimal position) in the presence of noisy and NLOS measurement error. The neural network uses its ability to memorize and generalize data to interpolate the measured angles to estimate the mobile position. In order to avoid over-fitting of the network, the training of the neural network was done with ideal patterns gotten from the mathematical relationship that exists between the angles of arrival and the mobile position. The performance of this technique is highly dependent on the noisy and NLOS measurements of AOA, and the mobile propagation environment.

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