Modeling FOG Drift Using Back-Propagation Neural Network Optimized by Artificial Fish Swarm Algorithm

Rui Song,Xiyuan Chen,Hong Zhang,Chong Shen

doi:10.1155/2014/273043

Rui Song, Xiyuan Chen + Show 2 more

Open Access

https://doi.org/10.1155/2014/273043

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Abstract

Based on the temperature drift characteristic of fiber optic gyroscope (FOG), a novel modeling and compensation method which integrated the artificial fish swarm algorithm (AFSA) and back-propagation (BP) neural network is proposed to improve the output accuracy of FOG and the precision of inertial navigation system. In this paper, AFSA is used to optimize the weights and threshold of BP neural network which determine precision of the model directly. In order to verify the effectiveness of the proposed algorithm, the predicted results of BP optimized by genetic algorithm (GA) and AFSA are compared and a quantitative evaluation of compensation results is analyzed by Allan variance. The comparison result illustrated the main error sources and the sinusoidal noises in the FOG output signal are reduced by about 50%. Therefore, the proposed modeling method can be used to improve the FOG precision.

Highlights

fiber optic gyroscope (FOG) is one kind of inertial sensors, which is based on the Sagnac effect and has been widely used in inertial system and engineering application at present
In 1980s, Shupe [3] had proved that a varied temperature which occurs to FOG will yield different refractive index of optical fiber in every sector of the fiber coil; the two slight beams produce a slightly different effective optical path; a nonreciprocal effect which is known as Shupe effect will be presented, which will bring a negligible error to the output and restricted FOG’s application
A new hybrid algorithm BP neural network optimized by artificial fish swarm algorithm (AFSA) is presented and used to describe the temperature drift characteristic of FOG

Summary

Introduction

FOG is one kind of inertial sensors, which is based on the Sagnac effect and has been widely used in inertial system and engineering application at present. Since the artificial fish swarm algorithm (AFSA) firstly proposed by Li et al [11] has been applied to many aspects successfully [5, 12, 13], the AFSA and BP neural network are firstly integrated to describe and model the FOG temperature drift. In order to improve the modeling performance of the neural network and reduce the random drift in FOG output, the wavelet package algorithm was applied to reprocess the datum [14] and the AFSA-BP neural network is applied to model the FOG temperature characteristics; at last the compensation result of temperature drift can be used to validate effectiveness of the proposed algorithm.

Theory and Experiments

AFSA-BP Algorithm

FOG’s Temperature Drift Modeling

Findings

Conclusion