Research on CSI Indoor Personnel Behavior Detection Algorithm Based on Adaptive Kalman Filter
DOI: 10.23977/iotea.2020.050101 | Downloads: 16 | Views: 441
Yanxing Liu 1, Shuyang Hou 1, Xiaoqin Li 1, Longyu Shi 1
1 College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
Corresponding AuthorYanxing Liu
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.
KEYWORDSbehavior 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.
 Zhou Q, Wu C, et al. Wi-Dog: Monitoring School Violence with Commodity WiFi Devices [J]. 2017.
 Wang X , Gao L, Mao S , et al. CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach [J]. IEEE Transactions on Vehicular Technology, 2018, 66 (1): 763-776.
 Xiao J, Wu K, Yi Y, et al. FIMD: Fine-grained Device-free Motion Detection [C] // 2012 IEEE 18th Interna- tional Conference on Parallel and Distributed Systems. IEEE Computer Society, 2012.
 MA L, WANG S T, MA D C, et al. Sparse representation based CSI indoor localization method[J]. Journal of Software, 2016 (27): 21-27.
 CHENG Yue, GE Xiyun, CAO Yuanshan. Indoor multi-sensor positioning algorithm based on improved location fingerprint algorithm [J]. Journal of Computer Applications, 2018, 38 (S2): 226-230.
 ZHANG Hai, FAN Qigao, ZHUANG Xiangpeng, et al. Pedestrian indoor positioning system based on strengthened adaptively outlier-restrained Kalman filtering [J]. Trans- ducer and Microsystem Technologies, 2019 (10): 106-109.
 Wang X, Gao L, Mao S. CSI Phase Fingerprinting for Indoor Localization with a Deep Learning Approach [J]. IEEE Internet of Things Journal, 2017, 3 (6): 1113-1123.
 Kotaru M, Joshi K, Bharadia D, et al. SpotFi: Decimeter Level Localization Using WiFi [J]. Acm Sigcomm Computer Communication Review, 2019, 45 (4): 269-282.
 DING Xuefang, WANG Qi. Wi-Fi indoor localization algorithm based on improved support vector machine. Computer Engineering and Applications, 2016, 52 (6): 90-93.
 Wang J, Xiong J, Jiang H, et al. Low Human-Effort, Device-Free Localization with Fine-Grained Subcarrier Information [J]. IEEE Transactions on Mobile Computing, 2018: 1-1.