针对数字磁罗盘(Digital Magnetic Compass,DMC)与光纤陀螺(Fiber Optic Gyro,FOG)单独使用时各自存在的缺点,为提高低成本航向测量方法的精度,提出DMC与FOG组合的概念,根据DMC与FOG的误差特性,设计了一种自适应卡尔曼滤波器将它们组合...针对数字磁罗盘(Digital Magnetic Compass,DMC)与光纤陀螺(Fiber Optic Gyro,FOG)单独使用时各自存在的缺点,为提高低成本航向测量方法的精度,提出DMC与FOG组合的概念,根据DMC与FOG的误差特性,设计了一种自适应卡尔曼滤波器将它们组合起来,获得最佳的指向精度。以低精度FOG真实静态漂移作为误差输入,进行了仿真实验,结果显示,自适应卡尔曼滤波误差收敛,能够很好地抑制DMC与FOG的偏置,同时平滑作用和信息融合作用效果明显,证明了此方法的有效性和可行性。展开更多
Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection ...Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.展开更多
文摘针对数字磁罗盘(Digital Magnetic Compass,DMC)与光纤陀螺(Fiber Optic Gyro,FOG)单独使用时各自存在的缺点,为提高低成本航向测量方法的精度,提出DMC与FOG组合的概念,根据DMC与FOG的误差特性,设计了一种自适应卡尔曼滤波器将它们组合起来,获得最佳的指向精度。以低精度FOG真实静态漂移作为误差输入,进行了仿真实验,结果显示,自适应卡尔曼滤波误差收敛,能够很好地抑制DMC与FOG的偏置,同时平滑作用和信息融合作用效果明显,证明了此方法的有效性和可行性。
基金This work is supported by the BK-21 FOUR program and by the Creative Challenge Research Program(2021R1I1A1A01052521)through National Research Foundation of Korea(NRF)under Ministry of Education,Korea.
文摘Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.
