CDM: Compound dissimilarity measure and an application to fingerprinting-based positioning

TL;DR

This paper introduces a novel non-vector-based dissimilarity measure called CDM, which improves fingerprinting-based indoor positioning accuracy by effectively handling collections of attribute/feature pairs with missing data.

Contribution

The paper proposes the compound dissimilarity measure (CDM) that combines vector metrics and set operations, allowing flexible, data-driven similarity assessment for fingerprinting-based positioning.

Findings

Positioning accuracy improves by about 5% using CDM.

2D RMSE errors are halved with CDM.

Over 10% increase in position solutions under 2m error.

Abstract

A non-vector-based dissimilarity measure is proposed by combining vector-based distance metrics and set operations. This proposed compound dissimilarity measure (CDM) is applicable to quantify similarity of collections of attribute/feature pairs where not all attributes are present in all collections. This is a typical challenge in the context of e.g., fingerprinting-based positioning (FbP). Compared to vector-based distance metrics (e.g., Minkowski), the merits of the proposed CDM are i) the data do not need to be converted to vectors of equal dimension, ii) shared and unshared attributes can be weighted differently within the assessment, and iii) additional degrees of freedom within the measure allow to adapt its properties to application needs in a data-driven way. We indicate the validity of the proposed CDM by demonstrating the improvements of the positioning performance of fingerprinting-based WLAN indoor positioning using four different datasets, three of them publicly available. When processing these datasets using CDM instead of conventional distance metrics the accuracy of identifying buildings and floors improves by about 5% on average. The 2d positioning errors in terms of root mean squared error (RMSE) are reduced by a factor of two, and the percentage of position solutions with less than 2m error improves by over 10%.