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An Indoor Continuous Positioning Algorithm on the Move by Fusing Sensors and Wi-Fi on Smartphones

机译:智能手机上融合传感器和Wi-Fi的室内移动连续定位算法

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Wi-Fi indoor positioning algorithms experience large positioning error and low stability when continuously positioning terminals that are on the move. This paper proposes a novel indoor continuous positioning algorithm that is on the move, fusing sensors and Wi-Fi on smartphones. The main innovative points include an improved Wi-Fi positioning algorithm and a novel positioning fusion algorithm named the Trust Chain Positioning Fusion (TCPF) algorithm. The improved Wi-Fi positioning algorithm was designed based on the properties of Wi-Fi signals on the move, which are found in a novel “quasi-dynamic” Wi-Fi signal experiment. The TCPF algorithm is proposed to realize the “process-level” fusion of Wi-Fi and Pedestrians Dead Reckoning (PDR) positioning, including three parts: trusted point determination, trust state and positioning fusion algorithm. An experiment is carried out for verification in a typical indoor environment, and the average positioning error on the move is 1.36 m, a decrease of 28.8% compared to an existing algorithm. The results show that the proposed algorithm can effectively reduce the influence caused by the unstable Wi-Fi signals, and improve the accuracy and stability of indoor continuous positioning on the move.
机译:当连续定位移动中的终端时,Wi-Fi室内定位算法会遇到较大的定位误差和较低的稳定性。本文提出了一种新颖的室内连续定位算法,该算法可以在移动中融合智能手机上的传感器和Wi-Fi。主要创新点包括改进的Wi-Fi定位算法和名为“信任链定位融合(TCPF)”算法的新颖定位融合算法。改进的Wi-Fi定位算法是根据移动中Wi-Fi信号的属性设计的,该属性可在新颖的“准动态” Wi-Fi信号实验中找到。提出TCPF算法来实现Wi-Fi和行人航位推算(PDR)定位的“过程级”融合,包括信任点确定,信任状态和定位融合算法三个部分。在典型的室内环境中进行了验证实验,移动中的平均定位误差为1.36 m,比现有算法减少了28.8%。结果表明,所提出的算法可以有效减少不稳定的Wi-Fi信号的影响,提高室内连续移动定位的准确性和稳定性。

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