LoRa-RSSI localization approach incorporating dynamic κ-µ fading modeling and maximum likelihood estimation
Theory and Technology|更新时间:2026-10-10
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LoRa-RSSI localization approach incorporating dynamic κ-µ fading modeling and maximum likelihood estimation
Chinese Journal on Internet of ThingsVol. 10, Issue 2, Pages: 156-164(2026)
作者机构:
内蒙古大学计算机学院,内蒙古 呼和浩特 010021
作者简介:
基金信息:
The National Natural Science Foundation of China(U25A20401);Inner Mongolia Science and Technology Program(2022YFSJ0027);The Natural Science Foundation of Inner Mongolia A.R. of China(2023MS06004);The Program for Young Talents of Science and Technology in Universities of Inner Mongolia A. R. of China(NJYT25011)
Chi Bo,Huang Baoqi,Jia Bing.LoRa-RSSI localization approach incorporating dynamic κ-µ fading modeling and maximum likelihood estimation[J].Chinese Journal on Internet of Things,2026,10(02):156-164.
Chi Bo,Huang Baoqi,Jia Bing.LoRa-RSSI localization approach incorporating dynamic κ-µ fading modeling and maximum likelihood estimation[J].Chinese Journal on Internet of Things,2026,10(02):156-164.DOI: 10.11959/j.issn.2096-3750.WLW25072.
LoRa-RSSI localization approach incorporating dynamic κ-µ fading modeling and maximum likelihood estimation