A Soft Range Limited K-Nearest Neighbours Algorithm for Indoor Localization Enhancement

TL;DR

This paper introduces SRL-KNN, a novel indoor localization algorithm that improves accuracy by incorporating spatial and temporal information without needing user speed or direction, achieving significant error reduction.

Contribution

The paper presents a soft range limited KNN algorithm that enhances indoor localization accuracy by integrating spatial range factors and RSSI histograms, without requiring user movement details.

Findings

Achieves an average localization error of 0.66 m.

Reduces localization errors by 45% compared to conventional KNN.

Maintains high accuracy without knowledge of user speed or direction.

Abstract

This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the unknown user location and the reference locations in the database. Different from that method, SRL-KNN scales the fingerprint distance by a range factor related to the physical distance between the user's previous position and the reference location in the database to reduce the spatial ambiguity in localization. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Moreover, to take into account of the temporal fluctuations of the received signal strength indicator (RSSI), RSSI histogram is incorporated into the distance calculation. Actual on-site experiments demonstrate that the new algorithm achieves an average localization error of $0.66$ m with $80\%$ of the errors under $0.89$ m, which outperforms conventional KNN algorithms by $45\%$ under the same test environment.