Improving Weighted Multiple Linear Regression Algorithm for Radiolocation Estimation in LoRaWAN

Abstract

In location based services, Weighted Multiple Linear Regression (WMLR) algorithm is used for radio device position estimation. Nevertheless, WMLR provides coarse location estimate, because weights apportioned to the received signal strength (RSS) for each hearable base station during matrix weight formation are not properly distributed. In an attempt to address the problem articulated above, an improved WMLR that enhanced the accuracy of radio device position estimate is proposed in this work. Min-Max scaling was used to determine the weight for each RSS values logged at different BS, as such forming a refined matrix weight. Public on-site outdoor Long Range Wide Area Network (LoRaWAN) RSS data set was used to assess the improved WMLR estimation algorithm on the basis of accuracy. The location accuracy of the proposed method is validated with the existing WMLR algorithm and Federal Communication Commission (FCC) maximum location error benchmark. Results show that the location accuracy of the improved approach outperformed that of the existing WMLR localization method.