Liu Wenta, Liu Jieyu, Shen Qiang. Integrated modeling and filtering of fiber optic gyroscope's random errors[J]. Opto-Electronic Engineering, 2018, 45(10): 180082. doi: 10.12086/oee.2018.180082
Citation: Liu Wenta, Liu Jieyu, Shen Qiang. Integrated modeling and filtering of fiber optic gyroscope's random errors[J]. Opto-Electronic Engineering, 2018, 45(10): 180082. doi: 10.12086/oee.2018.180082

Integrated modeling and filtering of fiber optic gyroscope's random errors

    Fund Project: Supported by National Natural Science Foundation of China (61503390)
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  • In order to analyze and process the random error of the fiber optic gyroscope (FOG) and improve its use precision, an error modeling method that combined empirical mode decomposition (EMD) and time series model was proposed. On the basis of the intrinsic mode functions (Imf) which was obtained by empirical mode decomposition, auto-regressive and moving average model (ARMA) modeling is performed hierarchically for each Imf. Then, Kalman filtering is performed layer by layer on the basis of the model to remove the random drift signals from the real angular velocity information. At the end of the algorithm, the signal which had been filtered need to be reorganized, and through the above steps, the conception of analyzing and modeling in connection with the random error of FOG from full frequency's point of view was realized. Compared with other modeling methods, this method has a higher degree of simulation matching to the original data, at the same time, the experimental results have further shown that this method can effectively remove the signal of random drift from the fiber optic gyroscope's output signal and improve its use precision significantly.
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  • Overview: Moving object detection has been the focus of research in the field of machine vision and intelligent transportation. Its purpose is to segment the moving objects from the sequence of video images so as to make the next step for target recognition, tracking and navigation. However, under the complex dynamic background, many factors such as light changes, background interference, camera motion and so on, make the detection very poor. At present, the commonly used feature point detection algorithms include SIFT, SURF and ORB algorithm, but they cannot meet the requirements of moving target detection. BRISK algorithm has better rotation invariance, scale invariance and better robustness. BRISK algorithm is the best one in the image registration with larger blur, but the real-time performance of BRISK algorithm is worse than ORB algorithm. Aiming at the real-time and accuracy of moving object detection algorithm, this paper proposes a moving object detection algorithm based on improved BRISK feature matching. Firstly, the video frames are divided into blocks, the entropy of each sub-block is calculated. The sub-blocks are filtered by using the image entropy, so that sub-blocks whose local information is too concentrated can be removed so as to avoid the influence of excessive local feature points. Secondly, the AGAST algorithm is used to detect the feature points of the remaining sub-blocks and generates the corresponding feature descriptors. Then, the feature matching is performed according to the k-nearest neighbor algorithm, and the feature point pairs are further purified by the Euclidean distance. So as to achieve the purpose of further improving the accuracy of the algorithm, and provide reliable data for calculating the next motion parameters. An improved PROSAC method is used to extract the optimal feature points to estimate the background motion parameters, and the background motion compensation is completed by combining the six-parameter affine model. Finally, the frame difference method and morphological process to extract the moving target, and the Otsu method is used to obtain the optimal threshold to achieve a more complete segmentation of the moving target. In order to evaluate the detection effect of the algorithm, three groups of video images are used to verify the algorithm. The proposed algorithm removes 32.7% of the feature points and improves the running time of 1.1 s based on the original BRISK algorithm. The detection efficiency is better than the previous ORB algorithm to some extent, at the same time improves the matching efficiency to more than 75%, and enhances the anti-noise performance of the algorithm. The experimental results show that the proposed algorithm can improve the real-time performance and ensure the robustness of the proposed algorithm. Compared with the previous detection algorithms, this algorithm is more suitable for the detection of moving objects in the complex dynamic context.

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