Estimating Object Location in RF Communication by Using RSSI Values Through k-NN and Deep Learning Techniques

Year 2025, Volume: 13 Issue: 3, 1331 - 1344, 30.09.2025

Nihat Daldal , Muhammad Zaib

https://doi.org/10.29109/gujsc.1705341

Abstract

GPS-based positioning faces significant challenges in accuracy and reliability, especially due to environmental factors such as signal interruptions, multi-path propagation, and poor satellite visibility. This study explores using RF signal strength (RSSI) to estimate object positions, comparing different algorithms in indoor and open-air environments. For indoor localization, the Mean Absolute Error (MAE) algorithm achieved a limited 66% success rate, primarily due to RSSI fluctuations caused by signal reflections from obstacles. In open-air settings, Neural Net Fitting (NNF) outperformed Machine Learning (ML). NNF demonstrated high accuracy of approximately 94.05%, indicating effective learning and minimal overfitting. The ML model achieved 74.4% accuracy, showing less stability and overall accuracy compared to NNF. Results suggest NNF is more effective for RF-based localization, particularly in open-air environments where signal propagation is less complex.

Keywords

RSSI , RF Positioning , Object localization , indoor-outdoor localiziation , wireless sensor networks

RF Haberleşmesinde RSSI Değerleri Kullanılarak k-NN ve Derin Öğrenme Yöntemleri ile Nesne Konumunun Tahmin Edilmesi

Abstract

GPS tabanlı konumlandırma, özellikle sinyal kesintileri, çoklu yol (multi-path) yayılımı ve zayıf uydu görünürlüğü gibi çevresel faktörler nedeniyle doğruluk ve güvenilirlik açısından önemli zorluklarla karşı karşıyadır. Bu çalışma, nesne konumlarını tahmin etmek için RF sinyal gücünü (RSSI) kullanmayı araştırmakta ve farklı algoritmaları kapalı alan ve açık hava ortamlarında karşılaştırmaktadır. Kapalı alan konumlandırmasında, Ortalama Mutlak Hata (MAE) algoritması, sinyallerin engellerden yansıması nedeniyle oluşan RSSI dalgalanmalarından ötürü %66 ile sınırlı bir başarı oranına ulaşmıştır. Açık hava ortamlarında ise Sinir Ağı Uydurması (NNF), Makine Öğrenimi'ne (ML) kıyasla daha iyi performans göstermiştir. NNF yaklaşık %94,05 gibi yüksek bir doğruluk oranı ile etkili öğrenme gerçekleştirmiş ve aşırı öğrenme (overfitting) göstermemiştir. ML modeli ise %74,4 doğruluk oranına ulaşarak NNF'ye kıyasla daha düşük kararlılık ve genel doğruluk sergilemiştir. Sonuçlar, sinyal yayılımının daha az karmaşık olduğu açık hava ortamlarında RF tabanlı konumlandırma için NNF'nin daha etkili olduğunu göstermektedir.

Keywords

RSSI , RF konumlama , Nesne konumlandırma , kapalı-açık alan konumlandırma , kablosuz sensor ağları

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