雷达回波资料反演海洋波导的算法和抗噪能力研究

利用海洋环境中大气波导的参数化模型及电磁波传播的地型抛物方程,建立电磁波传播正问题,获得电磁波传播功率损耗.结合正演模式,通过遗传算法反演出大气波导参数,并对遗传算法参数的设置进行了讨论,对反演系统进行了抗噪能力的分析.发现当误差小于10%时,遗传算法仍能保持较高的反演精度;当误差大于10%时,该算法的精度开始受到显著的影响.研究结果可能会对雷达仪器设计和应用提供参考. 关键词： 波导 电磁波传播方程 遗传算法 反演     

利用Bayesian-MCMC方法从雷达回波反演海洋波导

应用贝叶斯-蒙特卡罗（Bayesian-MCMC）方法将海洋波导参数的先验信息描述为先验概率密度,结合雷达回波资料（电磁波传播损耗）,得到待反演海洋波导参数的后验概率密度,用马尔可夫链蒙特卡罗（MCMC）-Gibbs采样器采样后验概率密度分布,并用样本最大似然估计值作为对海洋波导参数分布的估计.数值实验结果表明,该方法对先验信息进行了有效利用,反演精度高于遗传算法的反演精度.该方法较为充分利用先验信息,得到解的概率分布,即解的不确定性分析,这在实际应用中有一定的参考价值. 关键词： 波导 电磁波传播损耗 贝叶斯-蒙特卡罗 概率分布     

电离层电子总含量不同时间尺度的预报模型研究

电离层对无线电通信、卫星导航有重要的影响,因此对电离层电子总含量(total electron content,TEC)的预报研究十分重要,而目前国际上的各种经验电离层预报模型的精度只有60%左右,不能满足实际需求.本文提出一种新的TEC预报模型:利用经验正交函数对TEC数据进行时空分解,利用遗传算法结合混沌预测的思想对时间场系数进行非线性时间序列预测,从而达到对TEC数据预报的目的.实验结果表明,此方法可较好地对TEC数据进行不同时间尺度的预测,提前1,2,4,7 d的预报精度分别达到0.32,0.48,0.68,0.94 TECU.     

星载被动光学遥感大气风场探测技术进展综述

大气风场是表征整个地球大气系统动力学特征的重要参数,也是气象预报、空间天气、气候学等领域业务工作和科学研究必需的基础数据。被动光学遥感是大气风场测量领域的主要技术手段之一。本文综述了基于大气移动目标监测和大气光谱多普勒频移探测的两类天基被动光学大气风场测量技术的研究进展,主要介绍了云导风、红外高光谱水汽示踪、测风干涉仪和多普勒调制气体相关4种风场测量技术的基础物理原理和风速反演基本方法,根据每种星载被动光学测风技术体制分类及特点,介绍了代表性风场探测载荷技术研究进展及应用情况,探讨了星载被动光学大气风场探测技术的未来发展趋势。     

New reconstruction and forecasting algorithm for TEC data

To reconstruct the missing data of the total electron content(TEC) observations, a new method is proposed, which is based on the empirical orthogonal functions(EOF) decomposition and the value of eigenvalue itself. It is a self-adaptive EOF decomposition without any prior information needed, and the error of reconstructed data can be estimated. The interval quartering algorithm and cross-validation algorithm are used to compute the optimal number of EOFs for reconstruction.The interval quartering algorithm can reduce the computation time. The application of the data interpolating empirical orthogonal functions(DINEOF) method to the real data have demonstrated that the method can reconstruct the TEC map with high accuracy, which can be employed on the real-time system in the future work.     

变分伴随正则化方法从雷达回波反演海洋波导(Ⅱ):实际反演试验

在理论推导的基础上,分别利用模拟和实测的雷达回波资料进行反演试验.在模拟雷达回波资料反演时,分别对不引入正则化项和引入正则化项的反演结果进行讨论;在实际雷达回波资料反演时,讨论了反演结果对初猜值精度的依赖性,分别考虑初猜值为经验模型给定时和初猜值为统计反演结果时对最后反演精度的影响.最后比较了变分伴随正则化方法与传统统计反演算法的优缺点,指出下一步反演算法的改进方向.     

Monitoring of ducting by using a ground-based GPS receiver

In this paper,we describe the estimation of low-altitude refractivity structure from simulation and real ground-based GPS delays.The vertical structure of the refractive environment is modeled using three parameters,i.e.,duct height,duct thickness,and duct slope.The refractivity model is implemented with a priori constraints on the duct height,thickness,and strength,which might be derived from soundings or numerical weather-prediction models.A ray propagation model maps the refractivity structure into a replica field.Replica fields are compared with the simulation observed data using a squarederror objective function.A global search for the three environmental parameters is performed using a genetic algorithm.The inversion is assessed by comparing the refractivity profiles from the radiosondes to those estimated.This technique could provide near-real-time estimation of the ducting effect.The results suggest that ground-based GPS provides significant atmospheric refractivity information,despite certain fundamental limitations of ground-based measurements.Radiosondes are typically launched just a few times daily.Consequently,estimates of temporally and spatially varying refractivity that assimilate GPS delays could substantially improve over-estimates caused by using radiosonde data alone.     

The estimation of lower refractivity uncertainty from radar sea clutter using the Bayesian-MCMC method

The estimation of lower atmospheric refractivity from radar sea clutter(RFC) is a complicated nonlinear optimization problem.This paper deals with the RFC problem in a Bayesian framework.It uses the unbiased Markov Chain Monte Carlo(MCMC) sampling technique,which can provide accurate posterior probability distributions of the estimated refractivity parameters by using an electromagnetic split-step fast Fourier transform terrain parabolic equation propagation model within a Bayesian inversion framework.In contrast to the global optimization algorithm,the Bayesian-MCMC can obtain not only the approximate solutions,but also the probability distributions of the solutions,that is,uncertainty analyses of solutions.The Bayesian-MCMC algorithm is implemented on the simulation radar sea-clutter data and the real radar seaclutter data.Reference data are assumed to be simulation data and refractivity profiles are obtained using a helicopter.The inversion algorithm is assessed(i) by comparing the estimated refractivity profiles from the assumed simulation and the helicopter sounding data;(ii) the one-dimensional(1D) and two-dimensional(2D) posterior probability distribution of solutions.     

Estimation of lower refractivity uncertainty from radar sea clutter using Bayesian-MCMC method

Estimation of lower atmospheric refractivity from radar sea clutter (RFC) is a complicated nonlinear optimization problem. This paper deals with the RFC problem in a Bayesian framework. It uses unbiased Markov Chain Monte Carlo (MCMC) sampling technique, which can provide accurate posterior probability distributions of the estimated refractivity parameters by using an electromagnetic split-step fast Fourier transform terrain parabolic equation propagation model within a Bayesian inversion framework. In contrast to the global optimization algorithm, the Bayesian-MCMC can obtain not only the approximate solutions, but also the probability distributions of the solutions, that is, uncertainty analyses of solutions. The Bayesian-MCMC algorithm is implemented on the simulation radar sea-clutter data and the real radar sea-clutter data. Reference data are assumed to be simulation data and refractivity profiles obtained with a helicopter. Inversion algorithm is assessed (i) by comparing the estimated refractivity profiles from the assumed simulation and the helicopter sounding data; (ii) the one-dimensional (1D) and two-dimensional (2D) posterior probability distribution of solutions.