Much attention has been paid to the Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning. An early Taylor-series expansion location algorithm based on the RBF neural network (RBF-TSE) is proposed as the performance of TSE highly depends on the initial estimation. In order to have more accurate and lower costs, a new Taylor-series expansion location algorithm based on a 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. The algorithm using Self-Adaptive RBF neural network algorithm to correct TDOA measurements, then Taylor algorithm adopted the revised TDOA value to improve the position estimation. The algorithm does not need to make sure the number of RBF and center vector firstly. In the process, according to the distribution of the errors in the input space, the number of RBF adaptively increases and adjusts the center vector appropriately. Based on the corresponding deletion policy to make the number of RBF, the policy calls for the comprehensive evaluation of RBF network contribution firstly, and then deleting the small contributions to RBF, for the network structure always keeps it simple. It shows that the proposed Positioning algorithm has a strong inhibition of LOS/NLOS error of simulation results. Through the neural network of the LOS/NLOS error correction in NLOS channel environment, this algorithm has high location accuracy and thus the reliability of the positioning performance here is better than the positioning performance for Taylor algorithm, LS algorithm, Chan algorithm and RBF-TSE algorithm, and the required Hidden layer nodes for getting performance threshold are less than the RBF-TSE algorithm.
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Reference: Ren, J.; et al (2017). A Novel Positioning Algorithm Based on Self-adaptive Algorithm of RBF Network. The Open Electrical & Electronic Engineering Journal ., DOI: 10.2174/1874129001610010141