<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

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

School of Electronic and Information Engineering, North China University of Technology, No 5 Jinyuanzhuang Road, Shijingshan District, Beijing,

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In Non-Line-Of-Sight (NLOS) environment, location accuracy of Taylorseries expansion location algorithm degrades greatly. A new Taylor-series expansion...

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

Publicado en línea: 20 oct 2016

Páginas: 127 - 136

DOI: https://doi.org/10.1515/cait-2016-0059

Palabras clave
Position location, NLOS propagation, Adaptive processing, RBF neural network

© by Jin Ren

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Publicado en línea: 20 oct 2016

Páginas: 127 - 136

DOI: https://doi.org/10.1515/cait-2016-0059

Palabras clavePosition location, NLOS propagation, Adaptive processing, RBF neural network

© by Jin Ren

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
Position location, NLOS propagation, Adaptive processing, RBF neural network