An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency...An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.展开更多
模拟人类视觉感知机制,提出了一种基于多尺度自回归滑动平均(MARMA,multiscale autoregressive and moving average model)模型和Markov随机场(MRF,markov random field)的合成孔径雷达(SAR)图像分割新方法。首先,分析人类视觉感知系统...模拟人类视觉感知机制,提出了一种基于多尺度自回归滑动平均(MARMA,multiscale autoregressive and moving average model)模型和Markov随机场(MRF,markov random field)的合成孔径雷达(SAR)图像分割新方法。首先,分析人类视觉感知系统的工作机制和特点,利用SAR的成像机理,构建了SAR图像的金字塔结构和MARMA模型,以此模拟视觉过程中的空间尺度和朝向感知机制;然后,通过不同尺度上的MRF模型和改进的模拟退火(SA)算法实现更有效的多尺度分割策略。实验结果表明,本文提出的方法在SAR图像分割任务中有非常良好的表现。展开更多
基金Project supported by the National Key R&D Program of China (Grant No. 2022YFF0607504)。
文摘An absolute gravimeter is a precision instrument for measuring gravitational acceleration, which plays an important role in earthquake monitoring, crustal deformation, national defense construction, etc. The frequency of laser interference fringes of an absolute gravimeter gradually increases with the fall time. Data are sparse in the early stage and dense in the late stage. The fitting accuracy of gravitational acceleration will be affected by least-squares fitting according to the fixed number of zero-crossing groups. In response to this problem, a method based on Fourier series fitting is proposed in this paper to calculate the zero-crossing point. The whole falling process is divided into five frequency bands using the Hilbert transformation. The multiplicative auto-regressive moving average model is then trained according to the number of optimal zero-crossing groups obtained by the honey badger algorithm. Through this model, the number of optimal zero-crossing groups determined in each segment is predicted by the least-squares fitting. The mean value of gravitational acceleration in each segment is then obtained. The method can improve the accuracy of gravitational measurement by more than 25% compared to the fixed zero-crossing groups method. It provides a new way to improve the measuring accuracy of an absolute gravimeter.
文摘模拟人类视觉感知机制,提出了一种基于多尺度自回归滑动平均(MARMA,multiscale autoregressive and moving average model)模型和Markov随机场(MRF,markov random field)的合成孔径雷达(SAR)图像分割新方法。首先,分析人类视觉感知系统的工作机制和特点,利用SAR的成像机理,构建了SAR图像的金字塔结构和MARMA模型,以此模拟视觉过程中的空间尺度和朝向感知机制;然后,通过不同尺度上的MRF模型和改进的模拟退火(SA)算法实现更有效的多尺度分割策略。实验结果表明,本文提出的方法在SAR图像分割任务中有非常良好的表现。
