[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121839-en":3,"doc-seo-121839-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121839,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning-based Indoor Positioning Systems Using MultiChannel Information - Research Paper","Received signal strength indicator (RSSI) measures sensor power at the receiver and is widely used for indoor localization, yet its accuracy degrades significantly due to wall reflection/absorption and non-stationary factors such as doors and people. To reduce such positioning errors, this paper introduces an indoor positioning approach combining machine learning with channel state information (CSI) and fingerprinting. Experiments show improved average positioning accuracy, outperforming traditional RSSI-based localization with results up to 6.13% for random forest and 54.79% for back propagation neural networks.","Journal of Engineering and Technological Sciences  \nMachine Learning-based Indoor Positioning Systems Using MultiChannel Information  \nResearch Paper  \nShu-Hung Lee 1, Chia-Hsin Cheng 2, Tzu-Huan Hug\\\\2\\ Yung-Fa Huang3  \n1School of Intelligent Manufacturing and Automotive Engineering, Guangdong Business and Technology University, Qixingyan Scenic Area, Zhaoqing, Guangdong, 526020 China  \n2 Department of Electrical Engineering, National Formosa University, Wenhua Rd, Huwei, Yunlin, 632301 Taiwan 3 Department of Information and Communication Engineering, Chaoyang University of Technology, Jifeng E. Rd., Wufeng District, Taichung, 413310 Taiwan  \nCorresponding author: [yfahuang@cyut.edu.tw](yfahuang@cyut.edu.tw)  \nAbstract  \nThe received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6. 13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively.  \nKeywords: channel state information; indoor positioning; machine learning; RSSI; random forest; times.  \nIntroduction  \nNowadays, when people try to find an address, get lost, or are unsure of their current location, most of them will use the positioning service provided by the Google Maps App to locate and track the target points. The earliest positioning service technology can be traced back to the global positioning system (GPS) developed in the nineteenth century. The target in this technology uses received satellite signals to perform positioning algorithms to obtain 3D position information [1-4] . The positioning error decreases with an increase in the number of satellites that signals can be received. This method can accurately locate and track outdoor places but in complex environments, such as indoor places and deep mountain environments, the target cannot maintain the line of sight (LOS) with satellites, which makes the positioning accuracy not good enough [5-7] .  \nIn recent years, 5G mmWave technology has also been in use for localization in addition to GPS. Reference [8] proposed low-complexity channel estimation approaches in mmWave multiple-input single-output systems for accurate indoor positioning. Reference [9] provides a brief overview of the use of massive MIMO arrays for indoor localization in 5G, while a novel method for single-snapshot localization and mapping of the radio environment using a single-antenna receiver in 5G mmWave systems was proposed in Reference [10] .  \nIndoor positioning topology is carried out according to a positioning algorithm, and the metrics measured between the transmitting and receiving devices are taken as an index of positioning judgment. The metrics include time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), received signal strength indicator (RSSI), etc. TOA and TDOA require a high synchronization property for hardware devices [11-14], AOA requires a directional antenna [15-18], RSSI judges the signal strength between transceivers and the distance is measured by the logarithmic path loss method. Among them, the RSSI calculation is the most convenient. It does not require additional hardware equipment for measurement, so that related studies have been developed successively, such as enhancing the reliability of environmental changes to reduce data collection [19-22], data filt","cbCaig6QrDWrsnjE","https://ap.wps.com/l/cbCaig6QrDWrsnjE","pdf",1028940,1,11,"English","en",105,"# Introduction\n## Outdoor vs. Indoor Positioning Challenges\n## Emerging Technologies (e.g., 5G mmWave, Massive MIMO)\n## Positioning Metrics and Their Requirements\n## Limitations of RSSI-Based Localization\n## Machine Learning with CSI and Fingerprinting","[{\"question\":\"Why does RSSI-based indoor positioning suffer from lower accuracy?\",\"answer\":\"RSSI accuracy is affected by wall reflection and absorption and by non-stationary objects such as doors and people, which distorts the measured signal relationships.\"},{\"question\":\"What does the proposed approach combine to improve indoor localization?\",\"answer\":\"The method combines machine learning (random forest and back propagation neural networks) with channel state information (CSI) and fingerprinting.\"},{\"question\":\"How is the positioning performance evaluated compared with traditional methods?\",\"answer\":\"Experiments compare average positioning accuracy against traditional RSSI-based localization systems, showing improvements reported for random forest and BPNN.\"}]","Machine Learning-based Indoor Positioning Systems Using MultiChannel Information - 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