基于改进高斯滤波与加权环境参数自适应估计的定位方法

doi:10.12066/j.issn.1007-2861.1990

Journal of Shanghai University（Natural Science Edition） ›› 2019, Vol. 25 ›› Issue (5): 701-711.doi: 10.12066/j.issn.1007-2861.1990

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Location method based on improved Gaussian filter and adaptive estimation for weighted environment parameter

Yechen YANG^1,², Yueli HU^1,^2,³(), Jie XU^1,², Wenlong CHENG^1,², Huaibo YU^1,²

^1. Microelectronics Research and Develop Center,Shanghai University, Shanghai 200444, China
^2. Shanghai Key Laboratory of Power Station Automation Technology,Shanghai 200444, China
^3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Received:2017-07-09 Online:2019-10-30 Published:2019-10-31
Contact: Yueli HU E-mail:huyueli@shu.edu.cn

Abstract

Abstract:

Location technology based on received signal strength indication (RSSI) ranging is a kind of low-cost location technology. In order to reduce the error of RSSI value due to environmental factors, this paper proposes an improved weighted Gaussian filtering algorithm which is used to deal with the RSSI value, and it also proposes an adaptive estimation algorithm for weighted environment parameter which is used to estimate the environmental parameters of the place where the mobile node to be located is. Then the path loss model is determined by the estimated environment parameters, and the location of the mobile node is estimated by the model. Experimental results show that the method can effectively improve the positioning accuracy of the system.

Key words: received signal strength indication (RSSI) ranging, improved weighted Gaussian filtering, weighted environment parameter, adaptive estimation

CLC Number:

TP393

Yechen YANG, Yueli HU, Jie XU, Wenlong CHENG, Huaibo YU. Location method based on improved Gaussian filter and adaptive estimation for weighted environment parameter[J]. Journal of Shanghai University（Natural Science Edition）, 2019, 25(5): 701-711.

Figures/Tables 6

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References 12

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Location method based on improved Gaussian filter and adaptive estimation for weighted environment parameter

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