Balkan Journal of Electrical and Computer Engineering

2147-284X 2147-284X

MUSA YILMAZ

Computer Software Electrical Engineering

Bilgisayar Yazılımı Elektrik Mühendisliği

RSSI Based Indoor Localization with Reduced Feature Dimension

Yıldırım

Mustafa Eren

BAHÇEŞEHİR ÜNİVERSİTESİ

01 30 2022

10 1 106 109 06 24 2021 01 28 2022

2013

Balkan Journal of Electrical and Computer Engineering

Wifi based indoor localization gains the interest of researchers for several purposes. Among various techniques, fingerprinting based on Wifi received signal strength indicator (RSSI) is a widely used feature in indoor localization because of its simplicity in implementation and minimal hardware requirement conditions. However, the amount of access points (AP) at which the RSSI is measured from in the network increases the computational load. This paper presents an alternative approach for dimension reduction in RSSI based indoor localization. We focus on recognizing the building and floor of the test user which is a multi-class problem for both cases. In a multiple class problem, inter-class differences are obtained by Manhattan distance in pair-wise manner. From each pair calculation, top-25 and top-50 features with the largest variances are chosen and merged to generate the final feature set. The proposed algorithm is implemented and evaluated on UJIIndoorLoc dataset. According to the outcomes, our method provides 99.1% accuracy for building and 82.8% accuracy for floor estimation

Received signal strength indicator dimension reduction indoor localization UJIIndoorLoc dataset.

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