中圖分類號(hào)： TN911.23 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.200409 中文引用格式： 王欽銳，黃越洋，石元博,，等. NLOS環(huán)境下基于WSN的救援人員定位系統(tǒng)研究[J].電子技術(shù)應(yīng)用,，2020，46(12)：78-82,，88. 英文引用格式： Wang Qinrui,，Huang Yueyang，Shi Yuanbo,，et al. Research on the location system of rescuers based on WSN in NLOS environment[J]. Application of Electronic Technique,，2020，46(12)：78-82,，88.
Research on the location system of rescuers based on WSN in NLOS environment
Wang Qinrui1,，Huang Yueyang1，Shi Yuanbo2,，Zhang Jixiang1,，Zuo Ziyi1
1.The School of Information and Control Engineering，Liaoning Shihua University,，F(xiàn)ushun 113001,，China； 2.The School of Computer and Communication Engineering, Liaoning Shihua University,，F(xiàn)ushun 113001,，China
Abstract： During the process of large building disaster, due to the adverse effects of toxic smoke, noise, fire, electricity leakage, light and other factors, as well as the complex internal structure of large buildings, it is difficult for many rescuers to obtain reliable information. Considering the above situation, wireless sensor networks can play their advantages in positioning indoor complex environments. But there is a challenge. Although their positioning accuracy is very high in the LOS environment, their measurement may be polluted by non-line-of-sight propagation in the NLOS environment, which results in a decrease in positioning accuracy. To solve this problem, we propose an improved location method based on unscented Kalman filter(UKF). Firstly, the propagation state between mobile node and beacon node is identified by means of test statistics. Secondly, the linear Kalman filter(LKF) is used to measure the distance smoothly. On this basis, a modified Kalman filter(MKF) is used to weaken the influence of NLOS on the measurement. Then, the UKF method is used to determine the location information of the unknown mobile node. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation.
Key words : wireless sensor network；nonline-of-sight,；unscented Kalman filter,；location；rescue
