Impact of Preprocessing on Neural Network-Based RSS/AoA Positioning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Aghda, Omid Abbassi, Tomic, Slavisa, Belkacem, Oussama Ben Haj, Guerreiro, Joao, Souto, Nuno, Szczachor, Michal, Dinis, Rui
Format:	Preprint
Published:	2026
Subjects:	Signal Processing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

_version_	1866917279214600192
author	Aghda, Omid Abbassi Tomic, Slavisa Belkacem, Oussama Ben Haj Guerreiro, Joao Souto, Nuno Szczachor, Michal Dinis, Rui
author_facet	Aghda, Omid Abbassi Tomic, Slavisa Belkacem, Oussama Ben Haj Guerreiro, Joao Souto, Nuno Szczachor, Michal Dinis, Rui
contents	Hybrid received signal strength (RSS)-angle of arrival (AoA)-based positioning offers low-cost distance estimation and high-resolution angular measurements. Still, it comes at a cost of inherent nonlinearities, geometry-dependent noise, and suboptimal weighting in conventional linear estimators that might limit accuracy. In this paper, we propose a neural network-based approach using a multilayer perceptron (MLP) to directly map RSS-AoA measurements to 3D positions, capturing nonlinear relationships that are difficult to model with traditional methods. We evaluate the impact of input representation by comparing networks trained on raw measurements versus preprocessed features derived from a linearization method. Simulation results show that the learning-based approach consistently outperforms existing linear methods under RSS noise across all noise levels, and matches or surpasses state-of-the-art performance under increasing AoA noise. Furthermore, preprocessing measurements using the linearization method provides a clear advantage over raw data, demonstrating the benefit of geometry-aware feature extraction.
format	Preprint
id	arxiv_https___arxiv_org_abs_2602_16271
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Impact of Preprocessing on Neural Network-Based RSS/AoA Positioning Aghda, Omid Abbassi Tomic, Slavisa Belkacem, Oussama Ben Haj Guerreiro, Joao Souto, Nuno Szczachor, Michal Dinis, Rui Signal Processing Hybrid received signal strength (RSS)-angle of arrival (AoA)-based positioning offers low-cost distance estimation and high-resolution angular measurements. Still, it comes at a cost of inherent nonlinearities, geometry-dependent noise, and suboptimal weighting in conventional linear estimators that might limit accuracy. In this paper, we propose a neural network-based approach using a multilayer perceptron (MLP) to directly map RSS-AoA measurements to 3D positions, capturing nonlinear relationships that are difficult to model with traditional methods. We evaluate the impact of input representation by comparing networks trained on raw measurements versus preprocessed features derived from a linearization method. Simulation results show that the learning-based approach consistently outperforms existing linear methods under RSS noise across all noise levels, and matches or surpasses state-of-the-art performance under increasing AoA noise. Furthermore, preprocessing measurements using the linearization method provides a clear advantage over raw data, demonstrating the benefit of geometry-aware feature extraction.
title	Impact of Preprocessing on Neural Network-Based RSS/AoA Positioning
topic	Signal Processing
url	https://arxiv.org/abs/2602.16271