混响室雷达地杂波统计特性模拟

为实现混响室中威布尔分布杂波的有效模拟,营造逼真的雷达电磁环境,基于广义平稳非相关散射电磁环境下混响室信道衰落特性,通过控制幅度调制信号对衰落系数进行补偿,提出一种基于时域波形设计的混响室地杂波模拟方法。通过调节输入信号序列中大、小幅度脉冲比例,改变混响室传统输入信号的零均值幅度特性,以实测地杂波统计特性为参考,最终得到与实测数据统计特性参数一致的混响室杂波电磁环境。用最大似然估计和KS检验法对实验数据作参数估计和假设检验,实测地杂波数据拟合于标准威布尔模型,混响室实验数据拟合于实测地杂波幅度统计模型,从而实现了实测地杂波起伏特性的混响室有效模拟。     

基于小波leaders的海杂波时变奇异谱分布分析

海杂波的奇异谱分析不仅能从理论上揭示海洋表面的动力学机理,同时也是对海探测雷达的关键技术之一.本文提出基于小波leaders的海杂波时变奇异谱分析方法,将时间信息引入海杂波的奇异谱分析之中,从而实现动态的解析描述海杂波随时间变化的奇异谱特性.在理论上,通过信号自身加窗,将时间信息引入传统的奇异谱(或称多重分形谱),实现了对海杂波时变奇异谱分布分析;在算法上,充分利用了小波leaders技术对于多种奇异性的提取能力(包括chirp奇异性和cusp奇异性),通过对时变奇异性指数和时变尺度函数的Legendre变换,实现对海杂波时变奇异谱分布的计算;在应用部分,采用经典的多重分形模型——随机小波序列(RWC)以及三级海态条件下连续波多普勒体制雷达海杂波进行仿真分析,实验结果表明:1)基于小波leaders的奇异谱分布能跟踪海杂波的时变尺度特性,有效展示其时变奇异性谱分布;2)算法具有较好的负矩特性和统计收敛性.该方法能为复杂非线性系统及随机多重分形信号分析提供参考.     

含卷浪Pierson-Moscowitz谱海面电磁散射研究

海尖峰的存在会导致雷达虚警概率的上升和多目标环境中检测性能下降,因此研究海尖峰现象意义重大.海尖峰现象的一个重要特点是海面的水平极化散射强度接近甚至大于垂直极化散射强度,卷浪被认为是产生海尖峰的一个原因.首先建立了卷浪和Pierson-Moscowitz谱海面的共同模型,利用矩量法研究了卷浪模型的水平和垂直后向电磁散射特征,包括入射频率、入射角、风速和风向对电磁散射特征的影响.发现在小擦地角情况和较大风速下超级现象(水平散射强度大于垂直极化散射强度)比较明显,从而推论出在小擦地角入射下产生海尖峰现象的概率较大.同时对时变卷浪在小擦地角入射时的海杂波幅值分布特性和多普勒谱进行了分析.     

基于文氏改进谱的二维粗糙海面模型及其电磁散射研究

基于文氏改进功率谱和Donelan方向分布的实验结论,结合经典的Monte Carlo法提出了一种适用于不同水深、不同风浪成长阶段的二维粗糙海面模型.在经典的双尺度法计算海面后向散射的基础上,结合小斜率近似法的仿真结果,对电磁散射计算方法进行改进.通过与典型海杂波均值散射系数GIT模型的拟合结果进行对比,验证了文氏改进谱在中国近海海面模拟的适应性与改进后电磁散射计算方法的有效性.     

混沌海杂波背景下的微弱信号检测混合算法

基于经验模态分解理论, 提出了一种基于粒子群算法的支持向量机预测方法. 采用总体平均经验模式分解法将混沌信号分解为若干固有模态函数和趋势分量, 将复杂的非线性信号转化为具有不同尺度特征的平稳分量. 利用粒子群算法对支持向量机的惩罚系数和核函数进行优化, 结合支持向量机建立混沌序列的单步预测模型. 从预测误差中检测淹没在混沌背景中的微弱信号(包括瞬态信号和周期信号). 对Lorenz系统和实测IPIX雷达数据进行仿真实验, 结果表明, 该方法能够有效地从混沌背景噪声中检测出微弱目标信号, Lorenz系统得到的均方根误差0.000000339 (-102.8225 dB时)比传统支持向量机方法的均方根误差0.049 (-54.60 dB时)降低了5个数量级, 从海杂波中检测出具有谐波特性的微弱信号, 表明预测模型具有更低的门限和误差.     

海杂波的分段分数布朗运动模型

主要研究了分段分数布朗运动(PFBM)模型在雷达海杂波分形建模中的应用.由于自然界和人造系统中研究对象不具有数学上完美的分形特性, 从而研究对象的分形特性无法在整个尺度区间上成立, 传统上, 海杂波的单一分形模型仅利用无标度区间内海杂波的自相似信息进行参数估计, 并没有考虑海杂波在无标度区间以外的尺度下所包含的信息.分段分数布朗运动从频域角度对海杂波频谱进行分段描述, 对应到时域即从粗略尺度和精细尺度两方面描述海杂波时间序列.结合海杂波产生的物理背景, 该模型可以为海杂波时间序列在粗略尺度和精细尺度下表现出的不同粗糙度提供机理性解释.在此基础上, 还研究了具有不同多普勒频率的运动目标对海杂波的影响, 结果表明运动目标对粗略尺度和精细尺度下海杂波的影响程度是不同的.     

海杂波背景下的目标检测新方法

提出了一种基于分形布朗运动模型的S波段雷达海杂波分形维数提取方法.分析了基于记忆库混沌时间序列预测方法，引入一种改进核函数的支持向量机分类器.在此基础上，提出了一种新的海杂波背景下目标检测方法.应用S波段雷达实测海杂波数据，计算得到了该信号的分形维数与Lyapunov指数，验证了S波段雷达海杂波的混沌分形特性.仿真实验结果验证了该方法具有较强的检测能力和抗杂波性能. 关键词： 分形布朗运动 分形维数 记忆库预测方法 支持向量机分类器     

海杂波FRFT域的分形特征分析及小目标检测方法

针对海杂波背景下海情对小目标检测的严重影响, 本文研究了实测海杂波在分数阶Fourier变换(FRFT)域的分形特征, 分别提出了单、高尺度下的分形检测方法. 由数学定义推得, FRFT 在不同阶数和尺度情况下, 不具有一致的自相似特性, 采用多重分形趋势波动分析法确定分形参数H(q), 分析了海杂波在不同海情、距离和极化条件下的分形特征. 在单尺度基础上结合FRFT的变阶优势, 提出了阶数自适应的小目标检测方法; 高尺度条件下, 比较了不同因素对海杂波FRFT域多重分形参数的影响. 结果表明:海杂波FRFT域可用变换阶数的方法检测到湮没在复杂海情中的小信号, 检测门限多数提高200%以上, 比采用时域信号提高26.3%. H(q) 在负高尺度上具有明显的多重分形特征差异, H(q)-q曲线满足反正切分布, 纯海杂波与含目标数据的拟合幅值比分别大于1.8(HH)和1.4(VV), 为海杂波背景小目标检测提供了判定依据.     

海杂波背景下小目标检测的分形方法

针对海杂波背景下小目标检测对海情依赖性强的问题, 本文采用分数布朗运动模型对实测海杂波建模, 结合多重分形去势波动分析法确定分形参数, 分析了海杂波的单尺度、多重分形特性. 在单尺度分形的基础上, 利用表征海杂波分形特征的分数维和Hurst指数构建了分形差量, 提出了基于分形差量的小目标检测方法;在多重分形基础上, 比较了两种海杂波的高尺度多重分形特性. 结果表明, 当尺度q > 10时, 纯海杂波的多重分形参数H(q) < 0, 而存在小目标的H(q) > 0, 此差异性为高尺度分形参数的海杂波背景小目标检测提供了判定依据. 所研究的两种方法均能实现不同海情下的小目标检测.     

基于粒子群支持向量机的海杂波序列回归预测

在雷达数据处理中,为更好地抑制海杂波,预测海杂波是必要的;海杂波具有混沌特性,而支持向量机算法能够有效地对混沌序列进行回归预测,文章提出了一种改进的支持向量机海杂波序列回归预测算法;文中给出了算法的框架结构,采用了互信息法和改进的伪邻近点法提取海杂波混沌特性的延迟时间和嵌入维数,利用相空间重构求取SVM训练样本,应用改进的PSO算法优化SVM的核函数参数以及惩罚系数,并仿真了预测模型;仿真实验结果表明:海杂波回归预测能达到满意的精度,而PSO-SVM方法比SVM方法的预测精度更高。     

Forward and backward probabilistic inference of the sea clutter

The fast dynamics of the sea surface result in highly volatile time series of the sea clutter. Measures made by a moving sensor which observes the sea from different points of view cannot be compared directly if the clutter has significantly evolved during the sampling interval. The issue of transporting measures to a common time reference is addressed using a model in which the sea clutter and associated observables are homogeneous Markov processes described by stochastic differential equations. We solve the Fokker–Planck equations of the speckle and radar cross-section (RCS) to obtain their present to future transition probabilities, from which we derive those of the intensity and the real and imaginary parts of the reflectivity. Using Bayes’s formula and the independence property of the speckle and RCS, we show that the formula remain valid for the present to past transition probabilities. Numerical distributions are systematically computed and match the analytical distributions. The resulting two-way prediction capability can be used to probabilistically balance the dynamics of the sea clutter. A series of deterministic measures from different positions and times is transformed into a series of probabilistic measures from different positions at the same time.     

Stationary Response of Lotka-Volterra System with Real Noises

A stochastic version of Lotka-Volterra model subjected to real noises is proposed and investigated. The approximate stationary probability densities for both predator and prey are obtained analytically. The original system is firstly transformed to a pair of It o stochastic differential equations. The Ito formula is then carried out to obtain the It o stochastic differential equation for the period orbit function. The orbit function is considered as slowly varying process under reasonable assumptions. By applying the stochastic averaging method to the orbit function in one period, the averaged Ito stochastic differential equation of the motion orbit and the corresponding Fokker-Planck equation are derived. The probability density functions of the two species are thus formulated. Finally, a classical real noise model is given as an example to show the proposed approximate method. The accuracy of the proposed procedure is verified by Monte Carlo simulation.     

Chaotic dynamics of sea clutter

The notion that a deterministic nonlinear dynamical system (with relatively few degrees of freedom) can display aperiodic behavior has a strong bearing on sea clutter characterization: random-looking sea clutter may be the outcome of a chaotic process. This new approach envisages deterministic rules for the underlying sea clutter dynamics, in contrast to the stochastic approach where sea clutter is viewed as a random process with a large number of degrees of freedom. In this paper, we demonstrate, convincingly for the first time, the chaotic dynamics of sea clutter. We say so on the basis of results obtained using radar data collected from a series of extensive and thorough experiments, which have been carried out with ground-truthed sea clutter data sets at three different sites. The study includes correlation dimension analysis (based on the maximum likelihood principle) and Lyapunov spectrum analysis. The Lyapunov (Kaplan-Yorke) dimension, which is a byproduct of Lyapunov spectrum analysis, shows that it is indeed a good estimator of the correlation dimension. The Lyapunov spectrum also reveals that sea clutter is produced by a coupled system of nonlinear differential equations of order five or six. (c) 1997 American Institute of Physics.     

Onsager-Machlup Function for one dimensional nonlinear diffusion processes

Using Ito stochastic differential equations to describe stochastic processes the Onsager-Machlup Function of a nonlinear diffusion process is calculated. It is shown that for two examples the Onsager-Machlup Function calculated directly as limit of finite dimensional probability densities agrees with the formula derived by using the Ito calculus but differs from a formula given by Graham who used the concept of Langevin equations.     

Mathematical problems of modeling stochastic nonlinear dynamic systems

The purpose of this report is to introduce the engineer to the area of stochastic differential equations, and to point out the mathematical techniques and pitfalls in this area. Topics discussed include continuous-time Markov processes, the Fokker-Planck-Kolmogorov equations, the Ito and Stratonovich stochastic calculi, and the problem of modeling physical systems.     

Elimination of Fast Variables for a Class of Nonlinear Stochaqtic Dynamical Systems

A set of nonlinear stochastic differential equations (NSDE's) that describes a large class of nonlinear stochastic dynamical systems is studied. By virtue of the stochastic generalization of. usual adiabatic approximation, we obtain the solution of equation for the fast variable, and obtain a closed equation for the slow variable. The statistical properties of the-new stochastic variables occurred are studied. The formal NSDE's are treated in the Stratonovich sense and the Ito sense respectively.     

The influence of external noise on non-equilibrium phase transitions

The method of Ito stochastic differential equations is used to analyze the influence of external noise in non-equilibrium phase transitions. It is found that external noise deeply affects the behaviour of the system and gives rise to new phenomena not predicted by the deterministic analysis.     

The construction of an Ito model for geoelectrical signals

The Ito stochastic differential equation governs the one-dimensional diffusive Markov process. Geoelectrical signals measured in seismic areas can be considered as the result of competitive and collective interactions among system elements. The Ito equation may constitute a good macroscopic model of such a phenomenon in which microscopic interactions are adequately averaged. The present study shows how to construct an Ito model for a geoelectrical time series measured in a seismic area of southern Italy. Our results reveal that the Ito model describes the whole time series quite well, but it performs better when one considers fragments of the data set with lower variability range (absent or rare large fluctuations). Our findings show that generally detrended geoelectrical time series can be considered as approximations of Markov diffusion processes.     

Quantum Ito's formula and stochastic evolutions

Using only the Boson canonical commutation relations and the Riemann-Lebesgue integral we construct a simple theory of stochastic integrals and differentials with respect to the basic field operator processes. This leads to a noncommutative Ito product formula, a realisation of the classical Poisson process in Fock space which gives a noncommutative central limit theorem, the construction of solutions of certain noncommutative stochastic differential equations, and finally to the integration of certain irreversible equations of motion governed by semigroups of completely positive maps. The classical Ito product formula for stochastic differentials with respect to Brownian motion and the Poisson process is a special case.Parts of this work were completed while the first author was a Royal Society-Indian National Science Academy Exchange Visitor to the Indian Statistical Institute, New Delhi, and visiting the University of Texas supported in part by NSF grant PHY81-07381, and part while the second author was visiting the Mathematics Research Centre of the University of Warwick     

Fermion Ito's formula and stochastic evolutions

An Ito product formula is proved for stochastic integrals against Fermion Brownian motion, and used to construct unitary processes satisfying stochastic differential equations. As in the corresponding Boson theory [10, 11] these give rise to stochastic dilations of completely positive semigroups.Work completed in part while the first author was supported by an SERC research studentship, and in part while the second author was visiting the Physics Department of the University of Texas at Austin supported by NSF grant PHY 81-07381