A LSTM-RNN-Based Fiber Optic Gyroscope Drift Compensation

Ning Mao,Jiangning Xu,Hongyang He,Jingshu Li,Venkatesan Rajinikanth

doi:10.1155/2021/1636001

Abstract

Fiber optic gyroscope (FOG) inertial measurement unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in positioning and navigation of military and aerospace fields, due to its simple structure, small size, and high accuracy. However, noise such as temperature drift will reduce the accuracy of FOG, which will affect the resolution accuracy of IMU. In order to reduce the FOG drift and improve the navigation accuracy, a long short-term memory recurrent neural network (LSTM-RNN) model is established, and a real-time acquisition method of the temperature change rate based on moving average is proposed. In addition, for comparative analysis, backpropagation (BP) neural network model, CART-Bagging, classification and regression tree (CART) model, and online support vector machine regression (Online-SVR) model are established to filter FOG outputs. Numerical simulation based on field test data in the range of -20°C to 50°C is employed to verify the effectiveness and superiority of the LSTM-RNN model. The results indicate that the LSTM-RNN model has better compensation accuracy and stability, which is suitable for online compensation. This proposed solution can be applied in military and aerospace fields.

Highlights

Fiber optic gyroscope (FOG) is a kind of optical fiber sensor based on Sagnac effect, which is widely used in strapdown inertial navigation system (SINS) and engineering at present
Guo et al [14] used a BP neural network algorithm based on optimized prediction data, which could effectively reduce the influence of random white noise in FOG data on the model compensation accuracy
Conclusion is paper discussed a FOG drift modelling method based on LSTM-recurrent neural network (RNN). ree FOGs were employed for a temperature control experiment to test the proposed method

Summary

Introduction

FOG is a kind of optical fiber sensor based on Sagnac effect, which is widely used in strapdown inertial navigation system (SINS) and engineering at present. Some optimization algorithms and artificial neural networks [4] can better approximate nonlinear problems With this consideration, this paper is devoted to compensating FOG drift by using several fine artificial intelligence algorithms. Pan et al [13] applied the neural network method to the fitting and compensation of the FOG temperature drift model. Guo et al [14] used a BP neural network algorithm based on optimized prediction data, which could effectively reduce the influence of random white noise in FOG data on the model compensation accuracy. E BPBagging model of FOG temperature drift was established, which had better compensation effect than the linear regression model and BP neural network model. Erefore, we establish the LSTM-RNN model to compensate FOG drift.

Problem Statement

Theory of Modelling for FOG

Experimental Study