基于幅度与梯度综合信息的SAR图像非线性扩散去噪方法

探讨SAR图像相干斑抑制的非线性扩散方程方法.以抑制SAR图像噪声,提高图像质量.通过分析SAR图像的幅度分布特性,建立基于SAR图像幅度信息的非线性扩散方程,使方程在幅值较小的背景区域具有较大的光滑作用以抑制噪声,而在幅值较大的目标区域光滑作用较小以保护目标特征.同时为避免噪声对图像中幅度分布的影响,在每一步迭代之前,采用基于梯度信息的非线性扩散方程对图像进行预处理,得到了一种基于图像幅度和梯度综合信息的非线性扩散去噪方法.计算结果表明,本文方法在整体上均具有较好的去噪效果,去噪后的图像较传统方法具有更高的等效视数和边缘保持指数,既能充分抑制背景区域的噪声,又能保护目标点,还很少出现虚假目标.     

遗传算法设计梯度子实现优化的图象边界检测

计算机图象处理技术可广泛应用各种图象测试、模式识别和计算机视觉等领域，而图象边界检测是计算机图象处理的最基本和最低层的步骤之一。由于噪声的干扰和图象光照不均匀等因素的影响，目前的图象边界检测方法还不能有效地检测出各种不同模式的边界。本文提出了一种基于遗传算法和灰度梯试算子的图象边界检测法，通过对样本图象的训练，设计对训练样本模式最优的灰度梯度算子，增加了对噪声的抗干扰能力，并且使被检测出的边界更准确。     

一种改进的数字式波束形成的快速自适应算法

提出了一种改进的数字式波束形成的快速自适应算法,即具有严格约束最小功率的抽样矩阵梯度算法(CSMG)。它综合了传统抽样矩阵梯度、算法和具有严格约束的最小功率自适应算法的优点。计算机模拟表明,此算法波束方向随约束角度而变,自适应调零能力较强,收敛速度也较快。     

辅助平面法评定平面对平面倾斜度误差的数学模型

提出一种评定平面对平面倾斜度误差的新算法。该算法是通过确定辅助测量平面，在不含原理误差的条件下，快速、准确地确定符合其定义的倾斜度误差值。     

Semi-stochastic modification of second-order radial derivative of Abel–Poisson’s formula for validating satellite gravity gradiometry data

The geoid can be used to validate the satellite gravity gradiometry data. Validation of such data is important prior to their downward continuation because of amplification of the data errors through this process. In this paper, the second-order radial derivative of Abel–Poisson’s formula is modified stochastically to reduce the effect of the far-zone geoid and generate the second-order radial derivative of geopotential at 250 km level. The numerical studies over Fennoscandia show that this method yields the gradients with an error of 10 mE and when the long wavelength of geoid is removed from the estimator and restored after the computations (remove–compute–restore) the error will be in 1 mE level. We name this method semi-stochastic modification. The best case scenario is found when the degree of modification of the integral formula is 200 and the long wavelength geoid to degree 100 is removed and restored. In this case the geoid should have a resolution of 15′ × 15′ and the integration should be performed over a cap size of 3°.     

Relevant experience learning: A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.     

基于多源卫星融合资料的中国近海海洋锋检测研究

海洋锋是海洋动力学中重要的研究对象.目前主要利用卫星遥感海面温度数据分析海洋锋.由于夏季中国近海海域海面温度趋同特性,无法利用海面温度数据分析海洋锋,而卫星遥感海色数据可以有效弥补海面温度检测海洋锋的不足,提高海洋锋检测的准确性.基于多源卫星融合数据,利用梯度检测算法对中国近海海域进行海洋锋研究,结合海面温度和蓝绿波段...     

一种地球紫外中心快速提取方法

针对地球紫外中心指向高精度提取问题,提出一种基于梯度统计的快速、低存储需求的紫外地平圆盘中心提取算法。首先,考虑到星载计算机与高内存消耗的滤波算法、实时导航需求的冲突,采用结合Sobel边缘算子与局部二值模式（LBP）算子的改进边缘快速提取方法,有效剔除背景噪声并准确提取地球紫外临边特征。然后对非完整临边边缘采用最小二乘拟合得到中心的精确位置。实验结果表明,该方法抗噪声,可实现亚像素级地球中心提取,并显著降低存储需求和计算时间开销。对于1046×746的图像,该算法需要的存储空间仅为1100kByte,运算时间在20ms内,满足自主导航的需求。     

基于决策树的涡轴发动机气路健康评估方法

为解决涡轴发动机退化模式多、故障样本大、分类困难的问题,将梯度提升决策树(GB?DT)算法应用于涡轴发动机气路健康评估.首先对涡轴发动机故障标签进行了拆解,针对每一类标签设计了一个分类器.通过特征重要度排序筛选了6个分类标签的输入特征,有效降低模型复杂度.通过遍历确定树的最优数量和深度,确保了分类的准确性.仿真结果表明...     

Radial and latitudinal gradients of galactic cosmic rays in the heliosphere at solar maximum

Our understanding of galactic cosmic ray (GCR) modulation has advanced greatly in the last three decades. However, we still need an appropriate knowledge of the GCR intensity gradient. Numerical simulations of the transport particle equation allow interpretation of cosmic ray intensities in the heliosphere. We use the numerical solution of the GCR transport equation during solar maximum epoch to compute the radial and latitudinal gradients. Our analysis indicates that adiabatic energy loss plays an important role in the radial distribution of GCR in the inner heliosphere, while in the outer region the diffusion and convection are the relevant processes. The latitudinal gradient is small.