Indoor Positioning using Similarity-based Sequence and Dead Reckoning without Training

TL;DR

This paper introduces a novel indoor positioning method that combines similarity-based sequence matching with dead reckoning, eliminating the need for extensive training data and improving tracking efficiency.

Contribution

It presents a new approach that integrates sequence similarity and dead reckoning within a particle filter for indoor positioning without requiring prior training.

Findings

Effective tracking demonstrated through extensive experiments

Reduces time and labor compared to traditional fingerprinting methods

Achieves accurate indoor positioning without training phase

Abstract

For the traditional fingerprinting-based positioning approach, it is essential to collect measurements at known locations as reference fingerprints during a training phase, which can be time-consuming and labor-intensive. This paper proposes a novel approach to track a user in an indoor environment by integrating similarity-based sequence and dead reckoning. In particular, we represent the fingerprinting map as location sequences based on distance ranking of the APs (access points) whose positions are known. The fingerprint used for online positioning is represented by a ranked sequence of APs based on the measured Received Signal Strength (RSS), which is refereed to as RSS sequence in this paper. Embedded into a particle filter, we achieve the tracking of a mobile user by fusing the sequence-based similarity and dead reckoning. Extensive experiments are conducted to evaluate the proposed approach.