scholarly journals TOF Indoor Location Algorithm based on RBF Neural Network

2019 ◽  
Author(s):  
Hongmei Zhao ◽  
Jielei Zhao
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Indoor Location ◽  
Location Algorithm

scholarly journals A New Localization Algorithm Based on Taylor Series Expansion for NLOS Environment

2016 ◽  
Vol 16 (5) ◽  
pp. 127-136 ◽  
Author(s):  
Jin Ren ◽  
Jingxing Chen ◽  
Wenle Bai
Keyword(s):  
Neural Network ◽  
Series Expansion ◽  
Taylor Series ◽  
Rbf Neural Network ◽  
Taylor Series Expansion ◽  
Localization Algorithm ◽  
Location Accuracy ◽  
Location Algorithm ◽  
Adaptive Ability ◽  
The Impact

Abstract In Non-Line-Of-Sight (NLOS) environment, location accuracy of Taylorseries expansion location algorithm degrades greatly. A new Taylor-series expansion location algorithm based on self-adaptive Radial-Basis-Function (RBF) neural network is proposed in this paper, which can reduce the impact on the positioning accuracy of NLOS effectively on the basis of the measurement error correction. RBF neural network has a faster learning characteristic and the ability of approximate arbitrary nonlinear mapping. In the process of studying, RBF neural network adjusts to the quantity of the nodes according to corresponding additive strategy and removing strategy. The newly-formed network has a simple structure with high accuracy and better adaptive ability. After correcting the error, reuse Taylor series expansion location algorithm for positioning. The simulation results indicate that the proposed algorithm has high location accuracy, the performance is better than RBF-Taylor algorithm, LS-Taylor algorithm, Chan algorithm and LS algorithm in NLOS environment.

scholarly journals A Novel Positioning Algorithm Based on Self-adaptive Algorithm of RBF Network

2016 ◽  
Vol 10 (1) ◽  
pp. 141-148 ◽  
Author(s):  
Jin Ren ◽  
Jingxing Chen ◽  
Liang Feng
Keyword(s):  
Neural Network ◽  
Series Expansion ◽  
Taylor Series ◽  
Lower Cost ◽  
Rbf Neural Network ◽  
Taylor Series Expansion ◽  
Rbf Network ◽  
Location Algorithm ◽  
Positioning Algorithm ◽  
Self Adaptive

Much attention has been paid to Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning. A Taylor-series expansion location algorithm based on the RBF neural network (RBF-TSE) is proposed before to the performance of TSE highly depends on the initial estimation. In order to have more accurate and lower cost,a new Taylor-series expansion location algorithm based on Self-adaptive RBF neural network (SA-RBF-TSE) is proposed to estimate the initial value. The proposed algorithm is analysed and simulated with several other algorithms in this paper.

UWB indoor location algorithm based on improved BP neural network

2021 ◽  
Author(s):  
Hao Zhang ◽  
Yajun Zhao ◽  
Yixiao ZHANG ◽  
Jin Zuo ◽  
Min Bian ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Indoor Location ◽  
Location Algorithm

An Improved Indoor Location Algorithm Based on Backpropagation Neural Network

2022 ◽  
Author(s):  
Yaqin Xie ◽  
Teqi Wang ◽  
Ziling Xing ◽  
Hai Huan ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Backpropagation Neural Network ◽  
Indoor Location ◽  
Location Algorithm
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