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.     

实测海杂波光电图像混沌特性研究

基于随机信号模型的光电图像海杂波抑制是目前海杂波背景下目标检测常用方法,检测性能不甚稳定。在分析混沌动力系统相空间重构的基础上,通过提取实际海杂波光电图像序列的关联维数和最大Lyapunov指数,检验海杂波是否具有混沌性。实验结果表明:海杂波光电图像序列具有有限的关联维数和正的最大Lyapunov指数,验证了海杂波光电图像序列的混沌特性。海杂波具有的混沌特性使得可采用短时间预测法抑制海杂波,为海杂波光电弱目标检测提供新的解决思路。     

海杂波的多重分形特性分析

通过对实测海杂波数据进行多重分形测度分析，判定海杂波信号具有多重分形特性。本文进一步提取表征海杂波复杂特性的多重分形参数，研究了不同条件下海杂波多重分形特性的变化特点，探讨了基于多重分形特性的雷达目标检测方法的可行性，并给出了实验结果。本文的研究为基于多重分形特性的海杂波建模与仿真、雷达目标检测提供了依据。     

相关Weibull分布雷达杂波的建模与仿真

为了有效地在杂波背景下检测或处理雷达信号,介绍了零记忆非线性变换ZMNL法产生相关随机序列的方法.基于法的特点.详细分析并建立了Weibull分布的雷达杂波模型.且进行了计算机仿真.结果产生了具有高斯谱的相关Weibull分布杂渡,并给出了概率密度和功率谱密度的理论和实际仿真曲线,从而证明了方法能有效而准确地模拟雷达杂波.仿真得到的杂波序列可以直接应用到雷达环境特性的研究、雷达相干视频信号的模拟及对雷达回波信号的分析与检测.     

利用小波变换检测海杂波中信号的一种新方法

当雷达擦地角较小时,由于遮挡等因素的影响,海杂波中会出现所谓的“海尖峰”,对目标检测影响剧烈.文中提出一种新方法先利用小波变换对具有“海尖峰”特性的真实海杂波数据进行奇异性分析,在剔除奇异点后,再通过小波阈值处理进一步抑制杂波,最后基于Wigner-Ville时频分析方法检测信号.仿真结果表明了该方法在海杂波中检测信号的有效性.     

基于AR模型和ZMNL变换的K分布海杂波仿真方法

海杂波统计模型的研究对最优检测算法设计和雷达性能预估有重要作用。文章介绍了一种基于AR模型和ZMNL变换的K分布海杂波仿真方法,结合ACI准则和Yule-Walker方程,利用Levinson-Durbin递推关系式求解AR模型的阶数和参数,将生成的高斯序列通过线性滤波器产生服从K分布的相关序列。仿真结果表明,无论是功率谱还是概率分布都与理想分布相吻合。     

船载高频地波雷达一阶海杂波仿真模型研究

船载高频地波雷达(High Frequency Surface Wave Radar, HFSWR)海杂波仿真模型的建立对于有效海杂波抑制方法的提出具有重要指导意义。针对目前一阶海杂波模型存在的问题，提出了改进的船载HFSWR一阶海杂波空时分布模型。首先分析了船载平台在相参积累周期内前向运动对海杂波回波入射角的影响，提出了不同距离元海杂波子块入射角随时间变化的规律，并由此导出新的一阶海杂波时空分布模型、海杂波回波信号模型、以及目标回波模型。仿真结果表明，生成的海杂波数据的分布特性比传统模型更加接近实测海杂波的分布特性，为以后船载HFSWR海杂波有效抑制算法的研究提供较好的仿真数据支持。     

舰载雷达信号检测的计算机仿真

利用系统仿真工具对舰载雷达信号检测系统建模与仿真,可以经济、高效、快捷地对它的检测性能进行全面客观地综合评判。文中给出了如何利用蒙特卡洛(Monte-Carlo,简称MC)法来近似地描述研究对象的概率分布的一般方法。在此基础上,详细地介绍了如何利用蒙特卡洛法对舰载雷达信号检测系统进行建模与仿真,模拟出目标信号加海杂波的随机序列,同时给出了形状参数已知的Weibull分布海杂波背景下系统的仿真结果。该文提供的方法为研究海杂波背景下雷达系统的信号检测性能提供了灵活的试验平台。     

雷达海杂波性能分析及消除方法

文章根据舰载雷达海杂波影响情况及相关资料,对海杂波时域特性、频域特性、空域特性进行了分析。在分析的基础上给出了处理办法,并给出仿真结果。海杂波在时域上相关时间有限;海杂波在频域上类似高斯型。可以通过估计其参数进行自适应处理,在频域、空域及时域进行滤除,达到目标检测的目的。仿真结果表明,该种处理可达到滤除杂波的要求。     

高频双基地雷达海杂波数值仿真

海杂波是高频雷达海态信息的重要载体,也是影响高频雷达海面低速目标检测的重要因素,针对高频双基地雷达在定位精度精确性和反隐身问题上起重要作用.为了准确检测海杂波海态参数信息,利用数值仿真方法对双基地海杂波的特性进行研究.在分析双基地一阶和二阶海杂波形成机理的基础上,利用海杂波雷达散射截面方程,对高频双基地海杂波进行仿真.针对复杂的二阶谱仿真,采用固定波数下二阶子谱叠加方法,避免了求解非线性方程,并在仿真中采用角频率离散化方法,提高运算速度.仿真结果表明双基地海杂波不仅与雷达海态和雷达工作频率有关,还与双基地角密切相关.结果证明方法有效,为目标检测提供科学依据.     

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.     

检验统计量的选取对替代数据方法的影响

替代数据方法是检验时间序列中是否存在非线性的重要统计方法，它可对动力系统实测时间序列数据的随机性或混沌特性进行判定。在应用此方法时，我们发现单一的检验统计量往往不能作出接受或拒绝零假设的正确判断。因此本文引入互信息函数作为检验统计量，同高阶自相关函数和时间反演不对称统计量对时序进行检验的方法进行了比较。实验结果表明，三种统计量对时序的检验各有优势和局限性，交叉检验可获得较好的判断结果。     

Functional coefficient autoregressive models for vector time series

We extend the functional coefficient autoregressive (FCAR) model to the multivariate nonlinear time series framework. We show how to estimate parameters of the model using kernel regression techniques, discuss properties of the estimators, and provide a bootstrap test for determining the presence of nonlinearity in a vector time series. The power of the test is examined through extensive simulations. For illustration, we apply the methods to a series of annual temperatures and tree ring widths. Computational issues are also briefly discussed.     

Functional coefficient autoregressive models for vector time series

We extend the functional coefficient autoregressive (FCAR) model to the multivariate nonlinear time series framework. We show how to estimate parameters of the model using kernel regression techniques, discuss properties of the estimators, and provide a bootstrap test for determining the presence of nonlinearity in a vector time series. The power of the test is examined through extensive simulations. For illustration, we apply the methods to a series of annual temperatures and tree ring widths. Computational issues are also briefly discussed.     

Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India

Agricultural price forecasting is one of the challenging areas of time series forecasting. The feed-forward time-delay neural network (TDNN) is one of the promising and potential methods for time series prediction. However, empirical evaluations of TDNN with autoregressive integrated moving average (ARIMA) model often yield mixed results in terms of the superiority in forecasting performance. In this paper, the price forecasting capabilities of TDNN model, which can model nonlinear relationship, are compared with ARIMA model using monthly wholesale price series of oilseed crops traded in different markets in India. Most earlier studies of forecast accuracy for TDNN versus ARIMA do not consider pretesting for nonlinearity. This study shows that the nonlinearity test of price series provides reliable guide to post-sample forecast accuracy for neural network model. The TDNN model in general provides better forecast accuracy in terms of conventional root mean square error values as compared to ARIMA model for nonlinear patterns. The study also reveals that the neural network models have clear advantage over linear models for predicting the direction of monthly price change for different series. Such direction of change forecasts is particularly important in economics for capturing the business cycle movements relating to the turning points.     

Identification environment and robust forecasting for nonlinear time series

In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance.     

Testing for nonlinearity in mean and volatility for heteroskedastic models

A simple test for threshold nonlinearity in either the mean or volatility equation, or both, of a heteroskedastic time series model is proposed. The procedure extends current Bayesian Markov chain Monte Carlo methods and threshold modelling by employing a general double threshold GARCH model that allows for an explosive, non-stationary regime. Posterior credible intervals on model parameters are used to detect and specify threshold nonlinearity in the mean and/or volatility equations. Simulation experiments demonstrate that the method works favorably in identifying model specifications varying in complexity from the conventional GARCH up to the full double-threshold nonlinear GARCH model with an explosive regime, and is robust to over-specification in model orders.     

Improved estimation of electricity demand function by using of artificial neural network,principal component analysis and data envelopment analysis

Due to various seasonal and monthly changes in electricity consumption and difficulties in modeling it with the conventional methods, a novel algorithm is proposed in this paper. This study presents an approach that uses Artificial Neural Network (ANN), Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and ANOVA methods to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption. Pre-processing and post-processing techniques in the data mining field are used in the present study. We analyze the impact of the data pre-processing and post-processing on the ANN performance and a 680 ANN-MLP is constructed for this purpose. DEA is used to compare the constructed ANN models as well as ANN learning algorithm performance. The average, minimum, maximum and standard deviation of mean absolute percentage error (MAPE) of each constructed ANN are used as the DEA inputs. The DEA helps the user to use an appropriate ANN model as an acceptable forecasting tool. In the other words, various error calculation methods are used to find a robust ANN learning algorithm. Moreover, PCA is used as an input selection method, and a preferred time series model is chosen from the linear (ARIMA) and nonlinear models. After selecting the preferred ARIMA model, the Mcleod–Li test is applied to determine the nonlinearity condition. Once the nonlinearity condition is satisfied, the preferred nonlinear model is selected and compared with the preferred ARIMA model, and the best time series model is selected. Then, a new algorithm is developed for the time series estimation; in each case an ANN or conventional time series model is selected for the estimation and prediction. To show the applicability and superiority of the proposed ANN-PCA-DEA-ANOVA algorithm, the data regarding the Iranian electricity consumption from April 1992 to February 2004 are used. The results show that the proposed algorithm provides an accurate solution for the problem of estimating electricity consumption.     

An Out-of-Sample Test for Nonlinearity in Financial Time Series: An Empirical Application

This paper employs a local information, nearest neighbour forecasting methodology to test for evidence of nonlinearity in financial time series. Evidence from well-known data generating process are provided and compared with returns from the Athens stock exchange given the in-sample evidence of nonlinear dynamics that has appeared in the literature. Nearest neighbour forecasts fail to produce more accurate forecasts from a simple AR model. This does not substantiate the presence of in-sample nonlinearity in the series.