基于聚类分析和关联规则的降雨分析与实现

随着数字图像处理技术的发展,多角度、深层次挖掘卫星云图数据信息己成为一种必然发展趋势。根据卫星云图的特点应用了多种图像特征提取算法,包括图像的一、二阶距,分形维数特征和灰度梯度共生矩阵等算法。给出了K—means的改进算法,算法的出发点是确保发现聚类中心的同时使同一类内的相似度大,而不同类之间的相似度小。文中又引入了“事务模式”这一全新概念,基于它对Apriori算法改进后,算法用于云图和雨量数据间的关联规则挖掘,不仅获得了较好的实验结果,也满足了效率需求。     

CNG系列话音网关在油田卫星系统中的应用研究

介绍了使用CNG系列话音网关,通过中国石化Ku频段卫星实现油田边远小队话音通信联网。对原有设计方案的编号与计费方式提出了修改建议,阐述了CNG800和CNG300的主要功能、及电话交换机的联网方案,并详细分析了话音网关的参数配置方法。为通过卫星IP网络实现话音通信提供了又一应用实例。     

考虑非线性项的卫星编队保持

针对低轨道运行的编队卫星在非线性项作用下,伴随卫星运行的相对运动轨道发生偏移的情况,传统的李亚普诺夫方法能较好地控制轨道的初始偏移,使系统达到李亚普诺夫大范围一致稳定,但长期项误差震荡较大.本文提出了一种多回路结构的卫星轨道保持的控制方法,内回路选取使系统满足李亚普诺夫大范围一致稳定的控制律,外回路选取LQR线性状态调节器.此内外回路调节器能更好地抑制环绕卫星相对于基准轨道的误差漂移,更好地稳定编队队形.最后,数值仿真结果验证了此方法比传统的李亚普诺夫方法具有使编队更稳定的效果.     

多进制小波的遥感影像融合对比分析

提出了利用多进制小波进行融合的方案,目标是为了深度挖掘小波在遥感影像像素级融合方面的应用。采用多进制小波进行融合的优势是在减少能量损失的前提下使高频分量的分解方向更加细化,重构时频段组合更加灵活。首先从理论方面阐述了多进制小波和二进制小波(包)的异同,通过对比分析在像素级遥感影像融合实验结果,同时结合目视效果和多种评价指标,发现对于中分辨率——北京一号遥感影像,用多进制（M=4时）db5小波对全色和多光谱遥感影像进行融合的效果不亚于采用二进制小波(包)的融合效果。实验结果表明,小波融合的效果取决于小波基的选取、分解级数,也因处理对象而不同,但在采用相同的小波基的基础上,多进制小波比二进制小波(包)融合效果在目视效果和融合指标两方面都略胜一筹。其为小卫星数据在中国水资源、灾害、考古等领域的重点应用推广的数据处理方面打下基础,同时可为其他不同分辨率遥感影像的类似处理起到示范作用。     

一种气象卫星数据脉冲噪声滤除方法

气象卫星数据在采集或者传输的过程中,往往会引入不同程度的脉冲噪声。为提高图像质量,滤除噪音,进行可信度分析,提出了一种三阶段气象卫星数据脉冲噪声检测和滤除方法。在算法第一阶段,利用均值与均方差比值差值序列建立排序检测器(ROD),检测出图像中所有可能的脉冲噪声点;第二阶段,采用逐点滑动N×N窗口,按照每个可能的脉冲噪声点在不同的滑动窗口被重复检测到的次数,建立噪声可信度。第三阶段,对高噪声点进行滤波;对低噪声点迭代排序检测,如果可识别有噪声点,再进行滤波。结果表明,算法可以准确检测和滤除脉冲噪声,并保持数据中非噪声点信息。     

特征模型方法在中继星天线指向控制中的应用

主要研究基于特征模型的自适应控制方法在中继星天线指向跟踪控制中的应用.利用由Newton-Euler方法建立的挠性多体卫星的混合坐标方程动力学简化模型进行数学仿真,分别采用基于特征模型的自适应控制方法和经典PID方法进行卫星本体姿态和天线指向跟踪控制,并比较两种控制方法的控制稳定性和控制精度,仿真结果表明用特征模型自适应控制方法设计的控制器能满足系统性能要求,并具有很强的鲁棒性和抗干扰性.     

带有前置EDFA卫星相干激光通信终端的信噪比分析

卫星激光通信链路是一项实现卫星大规模星座组网的关键技术。相干激光链路灵敏度高、抗背景干扰、速率升级空间大,在星间激光链路中应用广泛。文章建立了带有前置掺铒光纤放大器(EDFA)的卫星相干激光通信终端的信噪比分析模型,仿真分析了EDFA功率增益、EDFA噪声系数、本振光功率、信号光功率、光放后端光带宽、基带前端电带宽对终端输出信噪比的影响以及各种输出噪声功率占比的情况,得到了带有前置EDFA卫星相干激光通信终端的信噪比参数特性。     

On the design considerations of solid-state power amplifiers for satellite communications: A systems perspective

Conventional solid-state power amplifier (SSPA) design approach isolates radio frequency (RF) design from communication theory. In this paper, a unified SSPA design approach is proposed, which optimizes SSPA parameters (bias voltage and input RF signal power) to minimize total DC power consumption while satisfying received SNR constraint specified by the link budget. The effect of SSPA nonlinearity is quantified by the error vector magnitude measured at its output and the corresponding received SNR degradation is analyzed. Using the quantitative metrics for received SNR, it is possible to evaluate highly nonlinear SSPA classes such as Class-B or deep-Class AB, which are normally not considered in conventional SSPA design approach to be used in satellite communication applications.     

Multi-objective routing algorithms for low-earth orbit satellite network

The low-earth orbit (LEO) satellite network, composed of a large number of satellite nodes, is a hot research topic at present. Due to the characteristics of the large-scale LEO satellite network, such as many satellite nodes, short orbit period, large dynamic change of topology, and unstable link-state, its communication quality of service (QoS) requirements are difficult to meet. Aiming at this problem, various factors that may affect data transmission are first analyzed. The network link selection problem is modeled as a multi-constraint optimization decision problem, a routing mathematical model based on linear programming (LP) is designed, and its solution is solved. Aiming at the problem of limited onboard computing resources, a multi-object optimization Dijkstra algorithm (MOODA) is designed. The MOODA finds the optimal path according to the comprehensive performance of the link. It solves the problems of poor comprehensive QoS performance and the low degree of load balancing of the paths found by the Dijkstra algorithm. The simulation results show that the paths found by the two algorithms have good QoS, robustness, and load balancing performance.     

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image Classification Algorithm (PICA) system is introduced to implement an image processing technique, detect disasters, and determine results with the help of the PICA, which allows disaster analysis to be extracted more effectively. The PICA is essential to overcoming strong shadows, for proper access to disaster characteristics to false positives by operators, and to false predictions that affect the impact of the disaster. The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches. Two types of proposed PICA systems detect disasters faster and more accurately (95.6%).