# Application of backpropagation neural networks to both stages of fingerprinting based WIPS

## TL;DR

This paper introduces a novel approach using backpropagation neural networks for both radio map construction and localization in fingerprinting-based indoor positioning, demonstrating improved performance over traditional methods.

## Contribution

The paper presents a dual-BPNN scheme for indoor positioning, enabling continuous radio map representation and enhanced localization accuracy, with extensive simulation validation.

## Key findings

- BPNNs outperform kNN methods in localization accuracy.
- Design parameter choices significantly affect BPNN performance.
- Real measurement data validates the effectiveness of the proposed scheme.

## Abstract

We propose a scheme to employ backpropagation neural networks (BPNNs) for both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal strengths (FWIPS): radio map construction during the offline stage, and localization during the online stage. Given a training radio map (TRM), i.e., a set of coordinate vectors and associated WLAN/WiFi signal strengths of the available access points, a BPNN can be trained to output the expected signal strengths for any input position within the region of interest (BPNN-RM). This can be used to provide a continuous representation of the radio map and to filter, densify or decimate a discrete radio map. Correspondingly, the TRM can also be used to train another BPNN to output the expected position within the region of interest for any input vector of recorded signal strengths and thus carry out localization (BPNN-LA).Key aspects of the design of such artificial neural networks for a specific application are the selection of design parameters like the number of hidden layers and nodes within the network, and the training procedure. Summarizing extensive numerical simulations, based on real measurements in a testbed, we analyze the impact of these design choices on the performance of the BPNN and compare the results in particular to those obtained using the $k$ nearest neighbors ($k$NN) and weighted $k$ nearest neighbors approaches to FWIPS.