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Research on CSI Indoor Personnel Behavior Detection Algorithm Based on Adaptive Kalman Filter

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DOI: 10.23977/iotea.2020.050101 | Downloads: 16 | Views: 441

Author(s)

Yanxing Liu 1, Shuyang Hou 1, Xiaoqin Li 1, Longyu Shi 1

Affiliation(s)

1 College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China

Corresponding Author

Yanxing Liu

ABSTRACT

In order to improve the accuracy of indoor personnel detection, we propose a channel state information (CSI) indoor personnel behavior detection algorithm based on adaptive Kalman filter in this paper. After collecting the original data package of CSI, the adaptive Kalman filter algorithm of variance compensation is used to filter the original data, and the dichotomous K-means clustering algorithm is used to classify the collected data and establish the fingerprint database. Then the k-nearest neighbor (KNN) matching algorithm is used to match the real-time data with the fingerprint database data to achieve the indoor behavior detection. The experimental results show that compared with the LIFS and FIMD methods, the method can recognize the action behavior of indoor people more accurately.

KEYWORDS

behavior detection, channel state information, k-means clustering algorithm

CITE THIS PAPER

Yanxing Liu, Shuyang Hou, Xiaoqin Li and Longyu Shi. Research on CSI Indoor Personnel Behavior Detection Algorithm Based on Adaptive Kalman Filter. Internet of Things (IoT) and Engineering Applications (2020) 5: 1-8. DOI: http://dx.doi.org/10.23977/iotea.2020.050101.

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