NLOS Identification for Indoor Localization using Random Forest Algorithm

Conference: WSA 2018 - 22nd International ITG Workshop on Smart Antennas
03/14/2018 - 03/16/2018 at Bochum, Deutschland

Proceedings: ITG-Fb. 276: WSA 2018

Pages: 5Language: englishTyp: PDF

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Authors:
Ramadan, Mohammed; Sark, Vladica; Gutierrez, Jesus; Grass, Eckhard (IHP, Im Technologiepark 25, Frankfurt (Oder), Germany)

Abstract:
Non-line-of-sight (NLOS) identification is a major challenge to indoor ranging and localization systems. Recently, many researchers have investigated this problem and some NLOS identification approaches have been proposed. In this paper, we exploit features extracted from the channel impulse response (CIR) and implement a random forest (RF) machine learning algorithm to tackle the NLOS identification problem.We evaluate the RF algorithm against the popular least squares-support vector machine (LS-SVM) and other state-of-the-art classification algorithms in terms of identification performance and computational complexity. Our evaluation results show that the proposed algorithm outperforms other classification algorithms and achieves NLOS and LOS identification accuracy of 97.3% and 95% respectively. Furthermore, it has smaller computational complexity than the LS-SVM, making it better suitable for realtime implementation.