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.
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Project supported by the National Natural Science Foundation of China (Nos. 51705324 and 61702332)
Yanfen LE and Heng YAO designed the research. Yanfen LE and Hena ZHANG processed the data. Yanfen LE drafted the manuscript. Weibin SHI helped organize the manuscript. Yanfen LE and Heng YAO revised and finalized the paper.
Compliance with ethics guidelines
Yanfen LE, Hena ZHANG, Weibin SHI, and Heng YAO declare that they have no conflict of interest.
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Le, Y., Zhang, H., Shi, W. et al. Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression. Front Inform Technol Electron Eng (2021). https://doi.org/10.1631/FITEE.2000093
- Indoor positioning
- Received signal strength (RSS) fingerprint
- Kernel ridge regression
- Cluster matching
- Advanced clustering