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A platform for research: civil engineering, architecture and urbanism
Two-stage clustering for improve indoor positioning accuracy
Abstract High accurate positioning, as a key factor for the most resource management algorithms, has attracted a lot of attention due to its crucial role in some new applications such as smart factories, IoT, and autonomous cars. Indoor positioning, as another major category of positioning in addition to outdoor positioning, is a very complicated process and achieving high accuracy is a tough process. This paper proposes a two-stage clustering-based approach (TSCA) for indoor positioning to deal with highly complex data sets which was collected from a large-scale indoor radio system. In the first stage, a new clustering algorithm, called group matching method is developed, which divides the dataset into several groups (sub-datasets) according to the different reference signal received power (RSRP) values of the real-world dataset. In the second stage, KNN and its variants, are used to match and evaluate the location of each device in one of sub-datasets instead of the entire dataset, which can increase the accuracy of the positioning. This method can perfectly solve the problem of uneven distribution of reference point data in the process of data acquisition, which is a popular challenge for most real scene data acquisition. The proposed method is compared with several state-of-the-art ML methods such as Support Vector Regression (SVR), and clustering methods such as K-means. The results indicate a high positioning accuracy improvement of more than 55% compared to a modified KNN method use our own RSRP fingerprint dataset, and a 2D accuracy improvement of 13.36% and a 3D accuracy improvement of 10.3% compared to a traditional KNN method use a Wi-Fi Received Signal Strength fingerprint.
Highlights Proposed a Two-Stage Clustering Based Approach (TSCA) to enhance the accuracy of indoor localization in large buildings. Developed an innovative Group Matching Method (GMM) to divide the dataset into several sub-datasets in the first stage. Matches and evaluates each device's location based on signal strength using a modified KNN method in the second stage. Using TSCA can improve accuracy by up to 55% compared to other indoor localization methods. Using Wi-Fi fingerprints, TSCA improves 2D and 3D accuracy by 13.36% and 10.3% compared to a KNN-based method.
Two-stage clustering for improve indoor positioning accuracy
Abstract High accurate positioning, as a key factor for the most resource management algorithms, has attracted a lot of attention due to its crucial role in some new applications such as smart factories, IoT, and autonomous cars. Indoor positioning, as another major category of positioning in addition to outdoor positioning, is a very complicated process and achieving high accuracy is a tough process. This paper proposes a two-stage clustering-based approach (TSCA) for indoor positioning to deal with highly complex data sets which was collected from a large-scale indoor radio system. In the first stage, a new clustering algorithm, called group matching method is developed, which divides the dataset into several groups (sub-datasets) according to the different reference signal received power (RSRP) values of the real-world dataset. In the second stage, KNN and its variants, are used to match and evaluate the location of each device in one of sub-datasets instead of the entire dataset, which can increase the accuracy of the positioning. This method can perfectly solve the problem of uneven distribution of reference point data in the process of data acquisition, which is a popular challenge for most real scene data acquisition. The proposed method is compared with several state-of-the-art ML methods such as Support Vector Regression (SVR), and clustering methods such as K-means. The results indicate a high positioning accuracy improvement of more than 55% compared to a modified KNN method use our own RSRP fingerprint dataset, and a 2D accuracy improvement of 13.36% and a 3D accuracy improvement of 10.3% compared to a traditional KNN method use a Wi-Fi Received Signal Strength fingerprint.
Highlights Proposed a Two-Stage Clustering Based Approach (TSCA) to enhance the accuracy of indoor localization in large buildings. Developed an innovative Group Matching Method (GMM) to divide the dataset into several sub-datasets in the first stage. Matches and evaluates each device's location based on signal strength using a modified KNN method in the second stage. Using TSCA can improve accuracy by up to 55% compared to other indoor localization methods. Using Wi-Fi fingerprints, TSCA improves 2D and 3D accuracy by 13.36% and 10.3% compared to a KNN-based method.
Two-stage clustering for improve indoor positioning accuracy
Lin, Huang (author) / Purmehdi, Hakimeh (author) / Fei, Xiaoning (author) / Zhao, Yuxin (author) / Isac, Alka (author) / Louafi, Habib (author) / Peng, Wei (author)
2023-06-09
Article (Journal)
Electronic Resource
English
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