Authors:
Hongmei Zhao
and
Jielei Zhao
Affiliation:
Zhengzhou University of Light Industry, China
Keyword(s):
Time of flight, None light of sight, RBF neural network, Particle swarm optimization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
In the UWB positioning system, due to the existence of multipath effects, NLOS and other factors, a certain degree of measurement error will result. In particular, the NLOS error has become a key factor affecting the positioning accuracy. Large NLOS errors often lead to a sharp decline in the positioning performance of UWB Indoor Positioning System. In this paper, a large number of data with NLOS error is used as a sample, and it is trained by RBF artificial neural network algorithm. In this way, the influence of the NLOS error can be eliminated at the source, and the positioning accuracy of the TOF can be improved.