Sea clutter modeling using an autoregressive generalized nonlinear-asymmetric GARCH model

The sea clutter modeling is critical to the radar design and assessment of relevant detection algorithms. In this paper, we investigate a family of generalized autoregressive conditional heteroscedastic (GARCH) processes to model the sea clutter as a time series, in which the current variance is dependent on historical information. The most general model (so-called the ALLGARCH model) provides more flexible variance structures to model non-Gaussian, asymmetry, and nonlinear properties of the clutter. However, after going through the usage of the ALLGARCH model, we find that it is not very suitable because the coefficients of the model, which are numerous, would be difficult to estimate in a real-time operating environment. Meanwhile, we find that some of the coefficients are negligible under almost all kinds of sea environments and weather conditions. Motivated by these observations, we propose a novel GARCH model for sea clutter modeling, which is a generalization of the nonlinear-asymmetric GARCH (NAGARCH) model. Considering the correlation between adjacent clutter returns, autoregressive terms are also introduced. By systematically analyzing practical sea clutter data under different sea environments, we demonstrate that the proposed model achieves comparable fitting effect to some commonly used statistical models. Also, we develop the corresponding generalized likelihood ratio test (GLRT) algorithm for the new model. Numerical simulations exhibit that the proposed detector achieves higher probability of detection, comparing with the AR-GARCH detector.     

Sea surface target detection based on complex ARMA-GARCH processes

In financial applications, it is common practice to fit return series by AutoRegressive Moving-Average (ARMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. In this paper, we develop a complex-valued ARMA-GARCH model for the sea clutter modeling application. Compared with the AR-GARCH model, the additionally introduced MA terms make the proposed model capable of considering the dependence of conditional variances of adjacent echo measurements as model coefficients, improving the modeling precision by taking advantage of the strong correlations between adjacent measurements. Based on the complex-valued ARMA-GARCH process for sea clutter modeling, we further develop a sea surface target detection algorithm. By analyzing a large number of the practical sea clutter data, we evaluate its performance and show that the proposed sea surface target detector offers a noticeable improvement for the probability of detection, comparing with the state-of-the-art AR-GARCH detector.     

海杂波背景下基于混沌理论的目标检测

根据海杂波具有混沌特性这一先验信息,利用RBF神经网络预测器的学习和对非线性函数的逼近能力,首先重构海杂波的内在动力学,然后引入一种基于预测误差的检测方法对微弱脉冲信号和矩形信号进行检测分析,最后得出了这种检测方法对微弱脉冲信号有较好的检测性能,信杂比改善可达43.3898 dB;而对矩形信号只在信号的起点和终点附近提高了检测性能,有类似的信杂比改善,中间各点检测不到信号,信杂比改善不大.     

海杂波背景下的小信号检测

利用海杂波信号为混沌这一先验知识,将判决反馈RBF神经网络应用于海杂波信号的建模与预测中,设计了一个三层判决反馈RBF神经网络结构.实验结果表明,判决反馈RBF网络模型对混沌时间序列有很好的拟合能力,对比典型RBF网络结构,该方法具有较好的检测效果.     

海杂波背景下的小信号检测

利用海杂波信号为混沌这一先验知识,将判决反馈RBF神经网络应用于海杂波信号的建模与预测中,设计了一个三层判决反馈RBF神经网络结构。实验结果表明,判决反馈RBF网络模型对混沌时间序列有很好的拟合能力,对比典型RBF网络结构,该方法具有较好的检测效果。     

Doppler distributed clutter model of airborne radar and its parameters estimation

1 Introduction As a kind of effective active sensor, airborne radar may realize detecting, tracking and imaging of targets on the sea, ground or at low altitude[1—4], and it has been widely used both in civil and military areas. Since airborne radar normally works at the look-down mode, the ground clutter is commonly strong, e.g. the clutter-to-noise ratio (CNR) may reach 60—90 dB in some mountainous or urban areas. Meanwhile, the clut-ter Doppler spectrum is center-shifted and width-spre…     

基于杂波强度在线估计的多目标跟踪算法

针对多目标跟踪中的未知杂波强度,提出了基于熵分布的杂波强度在线估计算法.利用有限混合模型对未知杂波强度建模,将仅依赖于混合权重的熵分布作为混合参数的先验;利用拉格朗日乘子法推导了混合权重在极大后验意义下的在线估计公式;以随机近似过程为在线估计策略,推导了基于缺失数据的分量均值和方差的在线估计公式.仿真结果表明,基于熵分布的杂波强度在线估计算法改进了概率假设密度滤波器在多目标跟踪中的性能.     

基于DEM的滨海地区非均匀杂波模拟

鉴于滨海地区军事地理位置的重要性以及场景复杂的多变性,对该区域的杂波模拟成为当前杂波研究工作的重点和难点。针对滨海地区杂波模拟这一问题,本文分析了影响滨海地区复合杂波生成的关键因素,并结合数字高程模型（DEM）,对地形场景进行了单元划分,重点对海陆交界杂波单元进行了后向散射单元系数建模,求解出该区域杂波功率谱。仿真结果表明,较之单纯的海面和地面,滨海环境下机载PD雷达的杂波功率谱非均匀起伏特性更为显著。仿真结果符合滨海地区杂波的理论分析,证明了基于DEM的滨海地区复合杂波模型和功率谱计算具有实用性和有效性。     

基于时频分析的海杂波背景下舰船目标检测

为了克服基于小波尺度谱重排的时频分析方法中时、频分辨率不佳及时频分布可读性较差等问题,本文提出了一种基于参数优化Morlet小波变换和奇异值分解的海杂波背景下舰船目标检测算法。算法首先利用Shannon小波熵作为目标函数,根据高频地波雷达信号的特点自适应地优化Morlet小波变换的时间带宽积参数,使得后续重排尺度谱的时、频分辨率同时达到最佳。然后再对重排小波尺度谱进行基于奇异值分解的降噪处理,以抑制环境噪声的影响,进一步提高时频分布的可读性。实验结果表明：与传统的时频分析算法相比,本文提出的算法具有更好的时频聚集性和较强的噪声抑制能力,能有效地检测海杂波背景下缓慢运动的匀速和匀加速舰船目标。     

基于拟蒙特卡罗的未知杂波GMP-PHD 滤波器

针对非线性系统模型中未知杂波环境下的多目标跟踪问题,提出一种基于拟蒙特卡罗方法的未知杂波高斯混合粒子概率假设密度(GMP-PHD)算法.首先利用有限混合模型拟合未知杂波空间分布,使其能够在杂波模型未知的情况下稳定跟踪目标;然后利用低偏差点集在状态空间中分布均匀的特性将拟蒙特卡罗采样方法应用到GMP-PHD中,使其在解决非线性滤波问题的同时提高目标跟踪精度.仿真实验表明,所提出的算法具有良好的跟踪性能.     

A simple multivariate ARCH model specified by random coefficients

This paper provides an alternative formulation of the conditional correlation structure in fitting the multivariate GARCH model. A special case is the multivariate ARCH model with random coefficients. Its coherence structure is derived by the correlations between the random coefficients which play an important role in describing the interested heteroscedastic features. The parameter estimation problem can be solved by maximum likelihood estimation and model selection is via the likelihood ratio test. We consider three real applications: (1) the spot and forward rates of the Deutsche Mark against the US dollars; (2) exchange rates of Deutsche Mark and Japanese Yen against US dollars; (3) the Heng Sang index and SES index.     

Design of a nonlinear controller based on a piecewise-linear Hammerstein model

This paper presents a nonlinear controller, which can be used to control single-input single-output nonlinear systems that can be approximated in terms of Hammerstein models. The proposed nonlinear controller is based on the principles of a classic linear pole placement controller and the piecewise-linear Hammerstein model. The controller can be used to control processes with highly nonlinear or even discontinuous static functions, while keeping simple controller structure and a very low computational burden.     

基于T- S模型的非线性系统多模型直接自适应控制

针对典型的高阶非线性系统，建立被控对象的多个论域不同的基于T—S模型的模糊控制器(TSFC)，用其加权组合控制系统的行为，并报据Lyapunov的综合方法设计一种自适应算法来调整每个TSFC的权值，形成被控对象的直接自适应模糊控制器。与采用单一TSFC的自适应模糊控制算法相比，该算法计算量小，响应速度快，能在局部上更有效地控制系统的非线性，使被控系统具有Lyapunov意义上的稳定性。仿真实验证实了算法的有效性。     

风洞系统非线性块状结构模型

本文将非线性块状模型的建模思想引入风洞系统模型的建立过程中,针对主排气阀和栅指电液伺服机构具有死区非线性特性,分别用含有死区输入的Hammerstein块状模型描述其动态特性,将主排气阀和栅指机构的输出作为风洞流场的输入,建立两输入两输出多变量耦合动态模型.两个独立的Hammerstein子模型与线性动态耦合的风洞流场模型串联构成一个非线性多变量块状模型.采用自适应加权递推辨识算法在线辨识Hammerstein子模型参数,采用带有遗忘因子的递推最小二乘法辨识风洞流场模型参数.仿真与风洞现场测试结果验证了本文方法的有效性.     

Parameter estimation in nonlinear systems with auto and crosscorrelated noise

The Gohberg-Heinig explicit formula for the inversion of a block-Toeplitz matrix is used to build an estimator of the inverse of the covariance matrix of a multivariable autoregressive process. This estimator is then conveniently applied to maximum likelihood parameter estimation in nonlinear dynamical systems with output measurements corrupted by additive auto and crosscorrelated noise. An appealing computational simplification is obtained due to the particular form taken by the Gohberg-Heinig formula. The efficiency of the obtained estimation scheme is illustrated via Monte-Carlo simulations and compared with an alternative that is obtained by extending a classical technique of linear system identification to the framework of this paper. These simulations show that the proposed method improves significantly the statistical properties of the estimator in comparison with classical methods. Finally, the ability of the method to provide, in a straightforward way, an accurate confidence region around the estimated parameters is also illustrated.     

Output regulation of nonlinear singularly perturbed systems based on T-S fuzzy model

1IntroductionNonlinear singularly perturbed systems arise in a widevariety of engineering applications,representative examplesinclude catalytic continuous stirred_tank reactors[1],biochemical reactors[2],fluidized catalytic crackers[3],flexible mechanical systems[4],electromechanical networks[5],etc.For such systems,the output regulation problem,i.e.the problem of having the output tracking reference(or rejecting disturbance)signals produced by someexternal generator,is ofimportance.In linear …     

A Weber–Haykin detector in correlated Pareto distributed clutter

A number of recent studies have shown that the Pareto distribution is a suitable alternative to the K-distributional model, for X-band maritime surveillance radar clutter. As such there has been interest in the development of non-coherent sliding window detectors in such a clutter environment. This is particularly the case when the clutter statistics exhibit spatial correlations. This paper examines a special case of the well-known Weber–Haykin detector, which is known to achieve the constant false alarm rate property in many cases of independent and identically distributed clutter. Since this is the situation in terms of Pareto Type I distributed clutter, the setting of thresholds for this detector is to be examined in correlated Pareto distributed clutter. Examples of performance analysis, in a particular multivariate Pareto model, are then considered.     

Output regulation of nonlinear singularly perturbed systems based on T-S fuzzy model

Based on the T_S model,the output regulation of nonlinear singularly perturbed systems via state feedback is discussed.It is shown that,under standard assumptions,this problem is solvable if and only if certain linear matrix equations are solvable.Once these equations are solvable, the state feedback regulator can easily be constructed.     

基于粒子滤波的非线性系统静态参数估计方法*

针对基于滤波方法的最大似然参数估计步长序列过于单一,算法收敛缓慢并很容易收敛于局部最优解的问题,提出了基于似然权值的在线EM参数估计算法(LWOEM)。通过粒子滤波方法实时估计系统的状态值变化,结合最大似然方法计算静态参数的点估计,然后通过计算更新参数的似然值来动态更新步长序列.与在线EM参数估计算法(OEM)的实验结果比较,表明该算法具有更好的适应性和收敛效果。     

CFI中基于动态区域划分的非平稳杂波抑制方法

在超声彩色血流成像(Color Flow Imaging,简称CFI)系统中,为抑制杂波信号对血流速度估计的影响,提出了一种基于动态区域划分的非平稳杂波抑制方法。该方法首先根据回波信号的能量特性提出动态区域划分法,将回波信号动态地分割为静态组织区、杂波区和血流区三部分;然后根据信号的时域特性,结合非平稳杂波抑制法和多项式回归法进行非平稳杂波抑制。仿真结果表明,该算法滤波后的杂波血流比约为6.120 dB,能较好地抑制非平稳杂波信号,估计出的血流速度剖面完整,成像质量较高;该算法的运行时间约为0.3414 s,执行速度为非平稳杂波抑制法的3倍,具有较高的实时性。