SLFNs interpolation fingerprint particle filter-based shared bicycle tracking algorithm. (30th April 2020)

• Record Type: Journal Article
• Authors: Cao, Honghua; Yan, Xiaoyan; Li, Yan
• Is Part Of: International journal of innovative computing and applications. Volume 11:Number 2/3(2020)
• Page Start: 135
• Page End: 140
• Publication Date: 2020-04-30
• Subjects: feed-forward network -- particle filter -- shared bicycle -- tracking algorithm -- strength indicator of signal; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

In order to improve the performance of traditional fingerprint detection method in the process of tracking the shared bicycle, the inertial sensor is used for data measurement. The particle filter (PF) method is a widely used sensor fusion algorithm, but the initialisation and weighting processes are problematic in shared bicycle positioning systems. In this paper, a new PF scheme is proposed, and it can produces smooth and stable localised knowledge. However, the feed-forward network that uses the single hidden layer is used to simulate the estimation and improvement of the performance of multiple probability to achieve the distinction of similar fingerprints. At the same time, the random sample consensus algorithm (RANSAC) is used to initialise the algorithm so as to reduce the convergence time. Experiments show that the tracking error of this scheme is less than 1.2 m, which is superior to the selected comparison method.