[1]XU Weichao,WANG Guanling,CHEN Mengyuan.Node localization of wireless sensor networks based on SR-CKF assisted by unmanned aerial vehicles[J].CAAI Transactions on Intelligent Systems,2019,14(3):575-581.[doi:10.11992/tis.201709019]
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Node localization of wireless sensor networks based on SR-CKF assisted by unmanned aerial vehicles

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 3 Page number: 575-581 Column: 学术论文—智能系统 Public date: 2019-05-05
Title:
Node localization of wireless sensor networks based on SR-CKF assisted by unmanned aerial vehicles
Author(s):
XU Weichao;  WANG Guanling;  CHEN Mengyuan
Anhui Key Laboratory of Electric Drive and Control, Anhui Polytechnic University, Wuhu 241000, China
Keywords:
unmanned aerial vehicle; wireless sensor network node; maximum likelihood estimation method; threshold selection; collaboration localization; square root volume kalman algorithm
CLC:
TN92;TP393
DOI:
10.11992/tis.201709019
Abstract:
Most applications of wireless sensor network nodes are distributed in the complex 3D terrain. When the wireless sensor network distribution scale reaches a certain extent, realizing the node positioning by loading the global positioning system (GPS) module on every sensor node becomes impractical. In view of this situation, this paper puts forward a kind of unmanned aerial vehicle (UAV)-assisted maximum likelihood estimation (MLE) method for the preliminary positioning of unknown nodes. We introduce the square root cubature Kalman filtering (SRCKF) algorithm for the precise positioning of unknown nodes and use the threshold selection update strategy to reduce the influence of nonlinear factors. The simulation results show that the UAV-WSN-MLE-SRCKF collaboration localization method proposed in this paper realizes the location estimation of unknown sensor nodes in the 3D terrain, reduces the cost of loading GPS modules to a large extent, and simultaneously improves the positioning accuracy and stability.
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