Modified Jaccard index analysis and adaptive feature selection for location fingerprinting with limited computational complexity. (3rd April 2019)

• Record Type: Journal Article
• Authors: Zhou, Caifa; Wieser, Andreas
• Is Part Of: Journal of location based services. Volume 13:Number 2(2019)
• Page Start: 128
• Page End: 157
• Publication Date: 2019-04-03
• Subjects: Fingerprinting-based indoor positioning -- adaptive forward-backward greedy algorithm -- feature selection -- modified Jaccard -- subregion selection; Mobile computing -- Periodicals; Multimedia systems -- Periodicals; Local area networks (Computer networks) -- Periodicals; Wireless communication systems -- Periodicals; 004.68
• DOI: 10.1080/17489725.2019.1577505
• Languages: English
• ISSNs: 1748-9725

Abstract

ABSTRACT: We propose an approach for fingerprinting-based positioning which reduces the data requirements and computational complexity of the online positioning stage. It is based on a segmentation of the entire region of interest into subregions, identification of candidate subregions during the online-stage, and position estimation using a preselected subset of relevant features. The subregion selection uses a modified Jaccard which quantifies the similarity between the features observed by the user and those available within the reference fingerprint map. The adaptive feature selection is achieved using an adaptive forward-backward greedy search which determines a subset of features for each subregion, relevant with respect to a given fingerprinting-based positioning method. In an empirical study using signals of opportunity for fingerprinting the proposed subregion and feature selection reduce the processing time during the online-stage by a factor of about 10 while the positioning accuracy does not deteriorate significantly. In fact, in one of the two study cases, the 90th percentile of the circular error increased by 7.5% while in the other study case we even found a reduction of the corresponding circular error by 30%.