The BP-Bagging model of FOG temperature drift was established, which had better compensation effect than the linear regression model and BP neural network model. In addition, the idea of ensemble learning is used by Liu et al. The experimental results indicated that the optimized models are effective and can improve the system’s performance. , and the particle swarm optimization (PSO) was applied by Wang, Cheng, and Tong et al. , the artificial fish swarm algorithm was applied by Song et al.
The genetic algorithm (GA) was applied by Pan et al. Therefore, some researchers have introduced some optimization algorithms to tackle the limitations.
It is difficult to find a suitable network structure, which may lead the generalization ability of the neural network insufficient. However, the establishment and learning process of the neural network belong to the nondeterministic polynomial (NP) problem. 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. However, random white noise in FOG output data was not considered, which might lead to overfitting of the network model. applied the neural network method to the fitting and compensation of the FOG temperature drift model.
The past few decades have seen the fruitful methods’ development for the compensation of FOG drift such as the neural network model, autoregressive integrated moving average (AIMA) model, adaptive compensation model, and deep learning model. With this consideration, this paper is devoted to compensating FOG drift by using several fine artificial intelligence algorithms. Some optimization algorithms and artificial neural networks can better approximate nonlinear problems. Studies have shown that the FOG drift has a strong nonlinearity, and the general approach of polynomial linear fitting methods cannot express the nonlinear characteristics of FOG exactly.
#FIBEROPTIC GYROSCOPE SOFTWARE#
The second one is compensation FOG drift in software by analyzing the test data, which is easy implementation and utility draws lots of the researcher’s attention. The first one is temperature control and compensation in hardware, which is efficient but high cost and complex. Therefore, there are two ways to compensate FOG drift. The accuracy of FOG is greatly affected by the surrounding environment, especially the environment temperature change, which will bring errors to the FOG output. In addition, FOG has been broadly used in military and aerospace fields because of its simple structure, small size, and high accuracy. IntroductionįOG 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. This proposed solution can be applied in military and aerospace fields. The results indicate that the LSTM-RNN model has better compensation accuracy and stability, which is suitable for online compensation. Numerical simulation based on field test data in the range of -20☌ to 50☌ is employed to verify the effectiveness and superiority of the LSTM-RNN model. 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. 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. However, noise such as temperature drift will reduce the accuracy of FOG, which will affect the resolution accuracy of IMU. 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.
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