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。     

基于综合识别系统的雷达测距不确定性的分析与仿真

雷达对目标进行探测,测量精度是一个重要的性能指标。影响一部雷达测量精度的因素是多方面的,本文着重讨论,在只有加型高斯白噪声的背景下,雷达测距的不确定性,并给出相应的仿真结果.     

An accessible implementation of interest rate models with Markov-switching

We examine the performance of interest rate models with regime-switching feature through a straightforward implementation. In particular, three short-rate models, the Vasicek, CIR and Black-Karasinski models, are extended to capture the switching of economic regimes using a finite-state Markov chain in discrete time. The Markov chain modulates the parameters of the model. We illustrate numerically that the resulting extended models are capable of reproducing various shapes of the yield curve. A quasi-maximum likelihood method based on James and Webber (2000) is employed to estimate the parameters of the regime-switching models. We demonstrate the implementation using actual financial datasets of Canadian yield rates. The numerical results show that under some model validation metrics, the two-state regime-switching models are more flexible, have better forecasting performance and provide better fit than the models without the regime-switching characteristic.     

基于分数阶矩和NM单纯形算法的海杂波参数估计

针对海杂波K分布形状参数和尺度参数的估计问题,提出了基于分数阶矩和NM单纯形算法的参数估计,首先采用分数阶矩估计法作为形状参数和尺度参数的初始值,然后利用NM单纯形算法进行数值搜索获得K分布形状参数和尺度参数的估计值。与实测的X波段海杂波概率密度函数对比表明,K分布优于Gamma分布和Weibull分布,其估计值与实测值最相吻合,且具有最小的均方根误差。对于一维的海杂波来讲,该方法是一种计算效率适中的算法。     

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.     

面向对象的机载雷达探测及干扰仿真

在该仿真系统中,通过对被仿对象的行为,特征等进行数学建模和仿真建模,特别是在干扰评估的数学建模中,导出了简单实用的数学模型,从而为准确的仿真打下了良好的基础。仿真系统采用ＪＡＶＡ语言面向对象编程。通过仿真可得到雷达在不同参数下的有效探测距离及干扰前后探测区域的变化,并能自动生成评估的表格。     

Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter

The performance of Bayesian state estimators, such as the extended Kalman filter (EKF), is dependent on the accurate characterisation of the uncertainties in the state dynamics and in the measurements. The parameters of the noise densities associated with these uncertainties are, however, often treated as ‘tuning parameters’ and adjusted in an ad hoc manner while carrying out state and parameter estimation. In this work, two approaches are developed for constructing the maximum likelihood estimates (MLE) of the state and measurement noise covariance matrices from operating input-output data when the states and/or parameters are estimated using the EKF. The unmeasured disturbances affecting the process are either modelled as unstructured noise affecting all the states or as structured noise entering the process predominantly through known, but unmeasured inputs. The first approach is based on direct optimisation of the ML objective function constructed by using the innovation sequence generated from the EKF. The second approach - the extended EM algorithm - is a derivative-free method, that uses the joint likelihood function of the complete data, i.e. states and measurements, to compute the next iterate of the decision variables for the optimisation problem. The efficacy of the proposed approaches is demonstrated on a benchmark continuous fermenter system. The simulation results reveal that both the proposed approaches generate fairly accurate estimates of the noise covariances. Experimental studies on a benchmark laboratory scale heater-mixer setup demonstrate a marked improvement in the predictions of the EKF that uses the covariance estimates obtained from the proposed approaches.     

雷达杂波模型的建立与仿真

杂波在雷达环境模拟中有着重要的作用,其特性可以用幅度分布特性和频率分布特性来描述.文章介绍了球不变随机过程SIRP和零记忆非线性变换ZMNL两种产生相关随机序列的基本方法.基于这两种方法各自的特点,详细分析并建立了对数正态分布、韦布尔分布以及K分布等几种概率分布的雷达杂波模型,且进行了计算机仿真.结果给出了概率密度和功率谱密度的理论曲线和实际仿真直方图,从而验证了这两种方法模拟雷达杂波特性的有效性和准确性.     

一种通用的雷达海杂波计算机仿真模型

针对雷达海杂波相干视频散射模型,给出了一种通用的计算机仿真模型.提出用迭代算法和随机向量法完成无记忆非线性变换过程,产生非高斯相干随机序列.其中迭代算法用来计算非线性变换过程中高斯序列相关性的变化,而随机向量法用来完成线性变换, 仿真实验数据证明利用这种方法可精确地产生具有指定概率密度和功率谱的非高斯随机序列,逼真地复现既包含振幅又包含相位的雷达海杂波相干视频散射信号.同时给出了迭代算法使用过程中的注意问题及海杂波模型仿真的过程和结果.     

Blind maximum likelihood identification of Hammerstein systems

This paper is about the identification of discrete-time Hammerstein systems from output measurements only (blind identification). Assuming that the unobserved input is white Gaussian noise, that the static nonlinearity is invertible, and that the output is observed without errors, a Gaussian maximum likelihood estimator is constructed. Its asymptotic properties are analyzed and the Cramér–Rao lower bound is calculated. In practice, the latter can be computed accurately without using the strong law of large numbers. A two-step procedure is described that allows to find high quality initial estimates to start up the iterative Gauss–Newton based optimization scheme. The paper includes the illustration of the method on a simulation example. A theoretical analysis demonstrates that additive output measurement noise introduces a bias that is proportional to the variance of that additive, unmodeled noise source. The simulations support this result, and show that this bias is insignificant beyond a certain Signal-to-Noise Ratio (40 dB in the example).     

雷达高度计中最大似然算法的应用

首先从分析国外星载雷达高度计中的自适应处理器入手,介绍了最大似然算法的基本理论,旨在分析最大似然算法用于雷达高度计的技术方法,并对有关公式进行了详细的推导,并对国外有关文献中的一些公式的推导错误进行了纠正。     

Modelling nonlinear count time series with local mixtures of Poisson autoregressions

A novel class of nonlinear models is studied based on local mixtures of autoregressive Poisson time series. The proposed model has the following construction: at any given time period, there exist a certain number of Poisson regression models, denoted as experts, where the vector of covariates may include lags of the dependent variable. Additionally, the existence of a latent multinomial variable is assumed, whose distribution depends on the same covariates as the experts. The latent variable determines which Poisson regression is observed. This structure is a special case of the mixtures-of-experts class of models, which is considerably flexible in modelling the conditional mean function. A formal treatment of conditions to guarantee the asymptotic normality of the maximum likelihood estimator is presented, under stationarity and nonstationarity. The performance of common model selection criteria in selecting the number of experts is explored via Monte Carlo simulations. Finally, an application to a real data set is presented, in order to illustrate the ability of the proposed structure to flexibly model the conditional distribution function.     

一类软件可靠度新模型的统计推断

对软件可靠性的著名模型——JM模型进行了修改和研究,给出了模型中的参数的极大似然估计存在的充要条件。     

Maximum likelihood estimation for constrained parameters of multinomial distributions—Application to Zipf-Mandelbrot models

A numerical maximum likelihood (ML) estimation procedure is developed for the constrained parameters of multinomial distributions. The main difficulty involved in computing the likelihood function is the precise and fast determination of the multinomial coefficients. For this the coefficients are rewritten into a telescopic product. The presented method is applied to the ML estimation of the Zipf-Mandelbrot (ZM) distribution, which provides a true model in many real-life cases. The examples discussed arise from ecological and medical observations. Based on the estimates, the hypothesis that the data is ZM distributed is tested using a chi-square test. The computer code of the presented procedure is available on request by the author.     

IMM算法实现非线性状态估计的研究与仿真

研究交互式多模型算法的非线性状态估计性能,按照工作点把非线性系统线性化为多个子模型,建立多模型自适应状态估计器.利用Monte-Carlo仿真法将其与EKF和UKF算法在不同参数下的噪声抑止能力和鲁棒性进行了比较,并分析了马尔可夫参数和模型个数对算法性能的影响.仿真结果表明该算法能达到理想的估计精度、收敛速度、稳定性和鲁棒性,能克服单一估计器由于参数变化和外部扰动所造成的估计误差过大,甚至发散的问题,能覆盖大范围的参数不确定性.     

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…