Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression

We propose a novel indoor positioning algorithm based on the received signal strength (RSS) fingerprint. The proposed algorithm can be divided into three steps, an offline phase at which an advanced clustering (AC) strategy is used, an online phase of approximate localization at which cluster matchi...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering 2021-06, Vol.22 (6), p.827-838
Main Authors: Le, Yanfen, Zhang, Hena, Shi, Weibin, Yao, Heng
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Electrical Engineering
Electronics and Microelectronics
Euclidean geometry
Fingerprints
Indoor environments
Instrumentation
Localization
Matching
Networks
Regression
Signal strength
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Summary:We propose a novel indoor positioning algorithm based on the received signal strength (RSS) fingerprint. The proposed algorithm can be divided into three steps, an offline phase at which an advanced clustering (AC) strategy is used, an online phase of approximate localization at which cluster matching is used, and an online phase of precise localization with kernel ridge regression. Specifically, after offline fingerprint collection and similarity measurement, we employ an AC strategy based on the K -medoids clustering algorithm using additional reference points that are geographically located at the outer cluster boundary to enrich the data of each cluster. During the approximate localization, RSS measurements are compared with the cluster radio maps to determine to which cluster the target most likely belongs. Both the Euclidean distance of the RSSs and the Hamming distance of the coverage vectors between the observations and training records are explored for cluster matching. Then, a kernel-based ridge regression method is used to obtain the ultimate positioning of the target. The performance of the proposed algorithm is evaluated in two typical indoor environments, and compared with those of state-of-the-art algorithms. The experimental results demonstrate the effectiveness and advantages of the proposed algorithm in terms of positioning accuracy and complexity.
ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.2000093