{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T19:10:57Z","timestamp":1654110657470},"reference-count":42,"publisher":"IGI Global","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,10,1]]},"abstract":"<p>In the case of coal mine accidents, in order to ensure timely rescue of the suffering people in a complex environment of underground localization, focusing on Received Signal Strength Indicator (RSSI) in underground personnel positioning accuracy is low and the problem of dynamic tracing parameters changes. Therefore, using an improved gravitational search algorithm (GSA) for the weighted centroid localization that based on RSSI. Utilizing the log distance path loss model gets the distance between the beacon nodes and unknown nodes, and then through the weighted centroid localization algorithm perform the unknown node positioning. Finally, the improved GSA-PSO optimizes the preliminary location results and parameters. Proposed solutions to establish simulation model is verified in MATLAB, and use the on-chip system CC2430 chips experiment platform is established. Experimental results show the proposed method can improve both the positioning accuracy effectively and the adaptive ability of changeful environment.<\/p>","DOI":"10.4018\/ijapuc.2016100102","type":"journal-article","created":{"date-parts":[[2017,3,6]],"date-time":"2017-03-06T15:17:09Z","timestamp":1488813429000},"page":"21-74","source":"Crossref","is-referenced-by-count":0,"title":["Research on Underground Personnel Positioning Method Based on PSO-GSA Optimization"],"prefix":"10.4018","volume":"8","author":[{"given":"Ye","family":"Liu","sequence":"first","affiliation":[{"name":"Udutech Inc., Shijiazhuang, China"}]},{"given":"Tianze","family":"Li","sequence":"additional","affiliation":[{"name":"Udutech Inc., Shijiazhuang, China"}]},{"given":"Tao","family":"Gao","sequence":"additional","affiliation":[{"name":"North China Electric Power University, Shijiazhuang, China"}]},{"given":"Yuhan","family":"Wang","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}]},{"given":"JiaHui","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"2432","reference":[{"key":"IJAPUC.2016100102-0","doi-asserted-by":"publisher","DOI":"10.1016\/S1389-1286(01)00302-4"},{"key":"IJAPUC.2016100102-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2008.06.003"},{"key":"IJAPUC.2016100102-2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2010.05.010"},{"issue":"3","key":"IJAPUC.2016100102-3","first-page":"367","article-title":"An Adaptive Indoor RSSI Algorithm Based on Gravitational Search.","volume":"44","author":"S. 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