扩展卡尔曼滤波和不敏卡尔曼滤波在实时雷达回波反演大气波导中的应用

为了改善雷达回波反演大气波导(RFC)方面存在的单时次、单方位角反演的问题,提出利用扩展卡尔曼滤波和不敏卡尔曼滤波的反演算法对大气波导结构的多方位角实时跟踪反演. 在卡尔曼滤波方法中分别给出大气波导结构的参数化方程、观测方程、滤波算法的状态转移方程,最后导出滤波反演算法的迭代求解流程. 在大气波导结构不随时间变化和随时间变化的两种条件下,对扩展卡尔曼滤波和不敏卡尔曼滤波算法进行数值实验. 实验结果表明,不敏卡尔曼滤波更适用于RFC这高度非线性反演问题,它可能今后为大气波导结构多方位角实时跟踪反演的业务化运行提供理论基础与技术保证. 关键词： 大气波导 雷达回波 扩展卡尔曼滤波 不敏卡尔曼滤波     

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.     

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     

风浪因素对海洋波导雷达回波作用机理的研究

采用文氏谱模型描述近海海洋动力环境海面的粗糙特征, 并运用海浪谱理论得到风速和均方根高度偏差的关系. 进而应用修正的离散混合傅里叶变换方法和改进的雷达散射系数模型分别计算了电磁波传输损耗和雷达散射系数. 在此基础上, 通过数值计算分析了风浪因素对海洋波导环境雷达回波功率值的影响. 结果表明, 风浪因素对不同观测高度处的传输损耗的影响均不大; 风浪因素对雷达回波功率有显著影响, 并且相对于传输损耗, 雷达散射系数项受风浪因素影响较大因而导致较大的雷达回波功率的变化.     

大气波导条件下雷达海杂波功率仿真

考虑近海面大气折射率垂直梯度的变化, 采用曲面波频谱估计方法(CWSE)计算掠射角, 并结合修正的GIT海表反射率模型和雷达距离方程对大气波导条件下雷达海杂波功率进行仿真, 最后通过数值实验及与实测数据比较, 验证了CWSE方法的可行性. 关键词： 大气波导 雷达海杂波 曲面波频谱估计     

基于大气分层的激光测风雷达回波模型

从相干激光测风雷达原理入手,论述了激光雷达回波特性和多普勒效应在测风雷达中的应用。基于大气分层模型,考虑到激光束的传播及其与大气之间的相互作用,建立了激光测风雷达回波模型,模拟回波信号在大气中的传输过程,为激光测风雷达回波信号的模拟提供了理论基础。     

遗传算法结合正则化方法反演海洋大气波导

针对正则化方法在解决实际反演问题时既能克服问题的不适定性又可以很大程度上抑制噪声和误差的传播, 本文提出了利用遗传算法结合正则化方法的新算法, 在遗传算法适应度函数中引入正则化项来反演波导参数; 然后对算法进行仿真试验, 结果表明新算法与传统遗传算法相比具有较高的反演精度, 并指出当噪声误差小于10%时, 算法具有较强的“去噪”性能; 最后利用机载雷达在Wallops岛探测的海表面处局部回波资料进行反演试验, 将反演结果与实测大气折射率廓线进行比较, 说明该算法的有效性. 新方法为海洋大气波导反演研究提供了一种新思路.     

随机摄动强跟踪粒子滤波算法

如何解决粒子的退化问题和提高算法对突变状态的跟踪能力,是粒子滤波算法研究和应用中需要考虑的两个主要因素.传统的再采样算法虽然可以解决退化问题,但是容易导致粒子耗尽;扩展粒子滤波算法虽然可在一定程度上解决粒子耗尽问题,但其对突变状态的跟踪能力却不近人意;强跟踪粒子滤波算法可以提高对突变状态的跟踪能力,但却未能较好地改善粒子退化问题.针对上述问题,本文将随机摄动再采样方法引入强跟踪粒子滤波算法,提出了一种随机摄动强跟踪粒子滤波算法.当粒子退化问题严重时,对权值最大的粒子迭加随机摄动,用摄动粒子替换退化粒子以解决粒子退化问题,同时由于摄动粒子的加入增加了粒子集的多样性,可在一定程度上缓解粒子耗尽问题,提高算法对突变状态的跟踪能力.利用标准验证模型和分时恒定系统对所提出的算法进行了仿真验证,仿真结果证明了该算法的可行性和有效性.     

基于辅助粒子滤波算法的红外目标跟踪

针对红外目标在跟踪中计算复杂的问题,构建辅助粒子滤波算法。利用贝叶斯重要性采样算法,在权值大的粒子基础上引入辅助粒子变量,然后重新定义重要采样分布函数,防止重采样后粒子概率密度变化。两次加权计算,使粒子权值比仅用重采样的粒子权值变化更稳定,采样点最接近真实状态;同时不同权值粒子的概率阈值可作为粒子滤波是否完成的判断准则。在二维平面构造红外运动目标模型中,系统为零均值高斯白噪声。仿真数据表明:该算法在x,y方向的均方误差、画面处理时间、RM SE性能上优于粒子滤波算法和重采样粒子滤波算法。     

大气气溶胶粒子折射率虚部反演方法研究

提出了反演折射率虚部的方法.以光散射为原理的粒子计数器测量光学等效直径,其结果受折射率虚部的影响较大;以粒子飞行时间为原理的粒子计数器测量空气动力学直径,其结果不受折射率虚部的影响.利用两种仪器的测量结果受折射率影响的差异来反演大气气溶胶的折射率虚部.通过与其它独立的测量结果对比表明,该方法反演气溶胶折射率虚部是合理的.     

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.     

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.     

Inversion for atmosphere duct parameters using real radar sea clutter

This paper addresses the problem of estimating the lower atmospheric refractivity (M profile) under nonstandard propagation conditions frequently encountered in low altitude maritime radar applications. The vertical structure of the refractive environment is modeled using five parameters and the horizontal structure is modeled using five parameters. The refractivity model is implemented with and without a priori constraint on the duct strength as might be derived from soundings or numerical weather-prediction models. An electromagnetic propagation model maps the refractivity structure into a replica field. Replica fields are compared with the observed clutter using a squared-error objective function. A global search for the 10 environmental parameters is performed using genetic algorithms. The inversion algorithm is implemented on the basis of S-band radar sea-clutter data from Wallops Island, Virginia (SPANDAR). Reference data are from range-dependent refractivity profiles obtained with a helicopter. The inversion is assessed (ⅰ) by comparing the propagation predicted from the radar-inferred refractivity profiles with that from the helicopter profiles, (ⅱ) by comparing the refractivity parameters from the helicopter soundings with those estimated. This technique could provide near-real-time estimation of ducting effects.     

基于雷达海杂波的区域性非均匀蒸发波导反演方法

由于不同海域上空气象条件的不同, 海上蒸发波导在大尺度海面上空发生时通常是区域性非均匀的, 这一特性使得该环境中的电波传播特性相对于水平均匀的蒸发波导环境情况而明显不同, 因此, 进行区域性非均匀的蒸发波导探测反演对正确预测电波传播特性及提高雷达系统的工作性能具有重要的意义. 考虑到实际应用中蒸发波导信息获取手段的多样性, 将中尺度数值气象模式MM5预报的区域性蒸发波导修正折射率剖面作为先验信息, 提出了一种含该先验信息的区域性非均匀蒸发波导的雷达海杂波后验概率估计模型. 该模型使用主分量分析法对蒸发波导的水平非均匀性进行参数化建模, 然后通过贝叶斯理论将修正折射率剖面参数的先验概率分布、后验概率分布和似然函数联系起来, 利用雷达海杂波实现蒸发波导剖面参数的最大后验概率估计反演. 通过我国东海海域的实际区域性非均匀蒸发波导反演测试, 表明该模型能够以更高的精度实现区域性非均匀蒸发波导的反演.     

Refractivity estimation from radar sea clutter

This paper addresses the problem of estimating lower atmospheric refractivity under the nonstandard propagation conditions frequently encountered in low altitude maritime radar applications. The vertical structure of the refractive environment is modeled by using a five-parameter model, and the horizontal structure is modeled as range-independent. The electromagnetic propagation in the troposphere is simulated by using a split-step fast Fourier transform based on parabolic approximation to the wave equation. A global search marked as a modified genetic algorithm (MGA) for the 5 environmental parameters is performed by using a genetic algorithm (GA) integrated with a simulated annealing technique. The retrieved results from simulated runs demonstrate the ability of this method to make atmospheric refractivity estimations. A comparison with the classical GA and the Bayesian Markov Chain Monte Carlo (Bayesian-MCMC) technique shows that the MGA can not only shorten the inverse time but also improve the inverse precision. For real data cases, the inversion values do not match the reference data very well. The inverted profile, however, can be used to synoptically describe the real refractive structure.     

Ray tracing/correlation approach to estimation of surface-based duct parameters from radar clutter

This paper describes a technique to estimate surface-based duct parameters by using a simple ray tracing/correlation method. The approach is novel in that it incorporates the Spearman rank-order correlation scheme between the observed surface clutter and the surface ray density for a given propagation path. The simulation results and the real data results both demonstrate the ability of this method to estimate surface-based duct parameters. Compared with the results obtained by a modified genetic algorithm combined with the parabolic wave equation, the results retrieved from the ray tracing/correlation scheme show a minor reduction in accuracy but a great improvement on computation time. Therefore the ray tracing/correlation method might be used as a precursor to more sophisticated and slower techniques, such as genetic algorithm and particle filters, by narrowing the parameter search space and providing a comprehensive and more efficient estimation algorithm.     

区间衍生粒子滤波器

针对非线性、非高斯环境下具有不确定动态模型参数的系统状态估计问题,提出了一种新颖的区间衍生粒子滤波算法.该算法利用区间滤波生成的重要性密度函数,在系统状态转移概率密度的基础上,融入最新的系统观测数据,从而提高了对系统状态后验概率的逼近程度.为了进一步提高算法的实时性,提出一种类似光子衍射的粒子衍生过程,进而缓解了滤波精度与运算量之间的矛盾.通过陀螺/星敏感器组合定姿问题验证了该算法的有效性和鲁棒性.     

Modified GIT model for predicting wind-speed behavior of low-grazing-angle radar sea clutter

A modified GIT model for describing the variational trend of mean clutter reflectivity as a function of wind speed is proposed. It uses two slope adjustment factors and two critical wind-speed factors to define and adjust the increasing slope of reflectivity with respect to wind speed. In addition, it uses a constant factor to compensate the overall amplitude of clutter reflectivity. The performance of the modified GIT model has been verified on the basis of the L-band low-grazing-angle radar sea clutter data. The results are in good agreement with the experimental data, indicating that the model is more effective in predicting the wind-speed behavior of clutter reflectivity than the conventional GIT model, especially for lower and higher wind speeds. We believe that the proposed model can provide deeper insights into the relationship between radar sea clutter reflectivity and sea state conditions.