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I explained about our research so far. The field of indoor localization using UWB is highly useful in various industries due to its high-precision and robustness to obstacles. However, in these areas, the error caused by NLOS caused by inter-communication inter-terminal obstacles is the biggest problem that needs to be addressed by compensating for the errors caused by NLOS and increasing the measurement accuracy. Recently, various approaches to responding to errors have been attempted using deep learning, but this is mostly the study of NLOS by indoor obstacles, and there is not enough research to respond to NLOS by the human body. Therefore, we proposed a system to identify where the UWB device is on the body and apply range error mitigation differently depending on the location of the UWB device to mitigate errors caused by NLOS by the human body. As a result of the experiment, the UWB device location identifier showed the identification accuracy of 99.3%, 93.7%, 98.4%, 100%, and 94%, respectively, for tripod, hand, chest, thigh, and fore-head. Also, our proposed schemes mitigated errors on hand, chest, thigh and fore-head by 22.93%, 17.32%, 10.4%, and 16.56%, respectively, compared to when the effects of human body were not considered. The following study attempts to mitigate localization error according to the position of the UWB device on the human body, not to mitigate the range error. |
IEEE/ICACT20210211 Slide.15
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