A Local Machine Learning Approach for Fingerprint-based Indoor Localization

TL;DR

This paper introduces a local nonparametric machine learning approach for indoor localization using RSSI data, which sequentially narrows down location possibilities through a decision-tree-like classification process, demonstrating competitive results on a public dataset.

Contribution

The paper presents a novel local ML method for indoor localization that differs from global models by partitioning the space based on output variables, enhancing adaptability and accuracy.

Findings

Effective localization accuracy on UJIIndoorLoc dataset

Comparable or improved performance over global ML algorithms

Flexible tuning of localization resolution

Abstract

Machine learning (ML) solutions to indoor localization problems have become popular in recent years due to high positioning accuracy and low cost of implementation. This paper proposes a novel local nonparametric approach for solving localizations from high-dimensional Received Signal Strength Indicator (RSSI) values. Our approach consists of a sequence of classification algorithms that sequentially narrows down the possible space for location solutions into smaller neighborhoods. The idea of this sequential classification method is similar to the decision tree algorithm, but a key difference is our splitting of the dataset at each node is not based on features of input (i.e. RSSI values), but some discrete-valued variables generated from the output variable (i.e. the 3D real-world coordinates). The strength of our localization solution can be tuned to problem specifics by the appropriate choice of how to sequentially partition the the space of location into smaller neighborhoods. Using the publicly available indoor localization dataset UJIIndoorLoc, we evaluate our proposed method vs. the global ML algorithms for the dataset. The primary contribution of this paper is to introduce a novel local ML solution for indoor localization problems.