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

TL;DR

This paper demonstrates that a neural network approach using preprocessing features significantly improves 3D positioning accuracy in RSS/AoA-based systems, especially under noisy conditions, compared to traditional linear estimators.

Contribution

It introduces a neural network model that leverages geometry-aware preprocessing to enhance RSS/AoA positioning accuracy, outperforming conventional linear methods.

Findings

Neural network with preprocessing outperforms linear methods under RSS noise.

Preprocessing features improve model accuracy over raw measurements.

Approach maintains high performance even with increased AoA noise.

Abstract

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.