基于辛算法的二维电磁场散射问题的研究

辛算法是保持Hamilton系统辛结构的一种新的数值方法,由于 Maxwell方程是一无穷维Hamilton系统,因此可将辛算法用于电磁场模拟中.本文提出一种基于辛分块Runge-Kutta(PRK)方法的显式辛算法,并将它成功应用于二维电磁散射问题的计算中.通过对金属方柱散射场的数值模拟,比较了FDTD法和低阶辛算法(一阶和二阶),结果表明低阶辛算法不仅与FDTD法精度相当,而且可以减少存储空间和计算时间,尤其是一阶辛算法节省了大约的CPU时间,提高了计算速度,体现了该算法的优越性.     

ADAPTIVE NONPARAMETRIC DETECTOR ON CORRELATION GAUSSIAN NOISE

The problem of adaptive nonparametric detector in correlation Gaussian noise isconsidered.The nonparametric detectors are CFAR(Constant-False-Alarm-Rate)detectors,whenthe input received reference cells are IID(Identical Independent Distribution)variances.But the falsealarm probability(P_(fa))of the nonparametric detectors could not be constant if the samples of thereference range cells are not independent.A simple and easily implemented adaptive nonparametricdetection method is proposed in the paper.In orde to maintain CFAR the weight of the detection rangecell of the detector must be changed by different output values of the IIR filter for measurement of thecorrelation coefficient(p)of the input noise.In this paper the closed form expressions for detectionprobability(P_d)and P_(fa)of the weighted nonparametric detector are derived.The ARE(AsymptoticRelative Efficiency)of the weighted detectors is investigated.In the end the detection performance ofthe adaptive nonparametric detector is determined by Monte Carlo simulation on the digital computer.     

Improved Robust Techniques for Multiuser Detection in Non-Gaussian Channels

In many physical channels where multiuser detection techniques are to be applied, the ambient channel noise is known through experimental measurements to be decidedly non-Gaussian, due largely to impulsive phenomena. This is due to the impulsive nature of man-made electromagnetic interference and a great deal of natural noise. This paper presents a robust multiuser detector for combating multiple access interference and impulsive noise in code division multiple access (CDMA) communication systems. A new M-estimator is proposed for "robustifying" the detector. The approach is corroborated with simulation results to evaluate the performance of the proposed robust multiuser detector compared with that of the linear decorrelating detector, and the Huber and the Hampel M-estimator based detectors. Simulation results show that the proposed detector with significant performance gain outperforms the linear decorrelating detector, and the Huber and the Hampel M-estimator based detectors. This paper also presents an improved robust blind multiuser detection technique based on a subspace approach, which requires only the signature waveform and the timing of the desired user to demodulate that user's signal. Finally, we show that the robust multiuser detection technique and its blind adaptive version can be applied to both synchronous and asynchronous CDMA channels.     

基于雷达知识库的知识辅助恒虚警检测算法

在非均匀杂波背景下,传统的恒虚警检测算法,比如CA鄄CFAR,所选择的参考单元与待检测单元往往无法满足独立同分布的条件,导致背景杂波功率水平的估计值存在偏差,使得检测性能降低。针对上述问题,文中提出了一种基于雷达知识库的知识辅助恒虚警检测算法。首先,利用雷达环境知识来构建动态的雷达知识库;然后,利用雷达知识库中的先验信息来辅助参考单元的选择,提高背景杂波功率水平的估计准确性,从而降低非均匀背景带来的影响;最后,利用线性调频连续波雷达采集的实测数据对该算法性能进行了验证。结果表明:在非均匀杂波环境下,基于雷达知识库的知识辅助恒虚警检测算法比传统算法有更好的检测性能。     

A CFAR adaptive subspace detector for second-order Gaussian signals

We study the problem of detecting subspace signals described by the Second-Order Gaussian (SOG) model in the presence of noise whose covariance structure and level are both unknown. Such a detection problem is often called Gauss-Gauss problem in that both the signal and the noise are assumed to have Gaussian distributions. We propose adaptive detectors for the SOG model signals based on a single observation and multiple observations. With a single observation, the detector can be derived in a manner similar to that of the generalized likelihood ratio test (GLRT), but the unknown covariance structure is replaced by sample covariance matrix based on training data. The proposed detectors are constant false alarm rate (CFAR) detectors. As a comparison, we also derive adaptive detectors for the First-Order Gaussian (FOG) model based on multiple observations under the same noise condition as for the SOG model. With a single observation, the seemingly ad hoc CFAR detector for the SOG model is a true GLRT in that it has the same form as the GLRT CFAR detector for the FOG model. We give an approximate closed form of the probability of detection and false alarm in this case. Furthermore, we study the proposed CFAR detectors and compute the performance curves.     

一种基于自动删除算法的PN码捕获技术研究

针对多径干扰对频率选择性信道下伪码捕获性能的影响,提出了一种基于自动删除算法的PN码捕获方法。该串行相关捕获系统通过一个基于排序数据方差的自动删除单元平均恒虚警检测器来删除参考滑窗中的干扰样本,并且不需要多径干扰数目的先验信息。对系统的平均捕获时间及检测性能进行了计算与仿真,与传统的自适应捕获方法相比,改进后捕获算法的性能得到较大的改善。     

相关高斯噪声自适应非参量检测器

本文讨论了相关高斯噪声自适应非参量检测器。当输入噪声的采样满足独立、同分布(IID)条件时,不管分布是什么形式,非参量检测器均能保持虚警概率恒定(CFAR)。但是,许多类噪声并不能保持IID条件,检测器也就无法保持CFAR。本文提出一种简单、可行的自适应非参量检测器,能自动调节门限,使虚警概率(Pfa)趋于恒定。这种方法的关键在于用递归滤波器的输出值来测量噪声的相关系数(Pd),并由此改变秩值检测器的检测单元的权,使其Pfa趋于恒定。从而使在一定信噪比条件下发现概率(Pd)也趋于恒定。本文给出检测单元加权的非参量检测器的检测性能和渐近性能,然后还给出自适应非参量检测器的近似计算方法和计算机模拟结果。     

Analysis of the Generalized Order Statistics Constant False Alarm Rate Detector

In this paper, we present an architecture of the constant false alarm rate (CFAR) detector called the generalized order statistics (GOS) CFAR detector, which covers various order statistics (OS) and cell-averaging (CA) CFAR detectors as special cases. For the proposed GOS CFAR detector, we obtain unified formulas for the false alarm and detection probabilities. By properly choosing coefficients of the GOS CFAR detector, one can utilize any combination of ordered samples to estimate the background noise level. Thus, if we use a reference window of size N, we can realize (2^N - 1) kinds of CFAR processors and obtain their performances from the unified formulas. Some examples are the CA, the OS, the censored mean level, and the trimmed mean CFAR detectors. As an application of the GOS CFAR detector to multiple target detection, we propose an algorithm called the adaptive mean level detector, which censors adaptively the interfering target returns in a reference window.     

Robust radar detection of CA, GO and SO CFAR in Pearson measurements based on a non linear compression procedure for clutter reduction

This paper deals with the constant false alarm rate (CFAR) radar detection of targets embedded in Pearson distributed clutter. We develop new CFAR detection algorithms-notably cell averaging (CA), greatest of selection (GO) and smallest of selection SO-CFAR operating in Pearson measurements based on a non-linear compression method for spiky clutter reduction. The technique is similar to that used in non uniform quantization where a different law is used. It consists of compressing the output square law detector noisy signal with respect to a non-linear law in order to reduce the effect of impulsive noise level. Thus, it can be used as a pre-processing step to improve the performance of automatic target detection especially in lower generalised signal-to-noise ratio (GSNR). The performance characteristics of the proposed CFAR detectors are presented for different values of the compression parameter. We demonstrate, via simulation results, that the pre-processed compression procedure is computationally efficient and can significantly enhance detection performance.     

基于杂波跟踪的CFAR检测研究

通过设立专门通道将图像处理技术应用于雷达检测域杂波功率数据处理,对特征杂波进行跟踪,为恒虚警处理提供背景信息.本文介绍了对抗杂波边缘的自适应CFAR检测器设计中的研究工作,设计了基于杂波跟踪的杂波属性CF(clutter feature)-CFAR检测器和结构元素SE(structuring element)-CFAR检测器,对其检测性能进行了分析.并通过蒙特卡罗(Monte Carlo)仿真方法对上述两种检测器的检测性能进行了仿真.     

一种新的基于UMVE和OS的恒虚警检测算法

为了充分利用参考单元所提供的信息,减少恒虚警损失,该文基于无偏最小方差估计(UMVE)方法和有序统计(OS)方法,提出了一种新的恒虚警检测器(MOSUM-CFAR)。它的前沿和后沿滑窗分别采用UMVE和OS方法产生两个局部估计,再对二者求和得到背景功率水平估计。在Swerling II型目标假设下,文中推导出MOSUM-CFAR在均匀背景下虚警概率Pfa和检测概率Pd及多目标环境下检测概率Pd的解析表达式,并与其它方案作了比较。分析结果表明MOSUM-CFAR在均匀背景和多目标环境下均具有相当好的检测性能。     

UWB SAR非均匀区域目标检测方法

在超宽带合成孔径雷达(UWB SAR)图像目标检测中不仅会遇到均匀杂波区域，还会遇到叶簇区域、空旷区域及干扰目标等组成的非均匀杂波区域。恒虚警率(CFAR)目标检测是雷达目标检测的重要方法，而传统的CFAR检测对UWB SAR非均匀杂波区域目标检测效果较差。首先分析了叶簇区域、空旷区域及二者混合区域的杂波分布。然后针对UWB SAR的实际情况，运用智能索引变量的CFAR检测技术(VI—CFAR)，使得均匀杂波和非均匀杂波背景中目标检测都取得了较好的效果。最后，利用实际UWB SAR目标检测结果验证了VI—CFAR的有效性。     

Adaptive Detection and Interference Rejection of Multiple Point-Like Radar Targets

We present an adaptive algorithm aimed at detecting multiple point-like radar targets embedded in correlated Gaussian noise. The proposed detector modifies and improves the adaptive beamformer orthogonal rejection test (ABORT) idea to address detection of multiple targets. More precisely, it relies on the so-called two-step generalized likelihood ratio test (GLRT) design procedure implemented without assignment of a distinct set of secondary data. The newly proposed detector can guarantee the constant false alarm rate (CFAR) property and the performance assessment, conducted resorting to simulated data, has shown that it exhibits better rejection capabilities of mismatched signals than previously proposed detectors, at the price of an acceptable performance loss for matched signals     

复合高斯杂波中距离扩展目标的参数化Wald检测

本文研究复合高斯杂波环境中的距离扩展目标的自适应检测问题。有色杂波采用参数未知的自回归（AR）过程描述。结合Wald检测准则,仅需对H1假设条件下的未知参数进行最大似然估计,给出了一种新的基于参数化模型的扩展目标检测器——参数化Wald检测器。该检测器的检验统计量可解释为首先针对各个待测单元分别计算检验统计量,然后将所有待测单元的输出进行非相参累加,其对杂波的随机功率起伏具有恒虚警率(CFAR)特性。相比于常规的基于协方差矩阵的检测方法,参数化检测算法的执行过程不需要依赖辅助数据,仅利用待测扩展目标数据即可实现自适应处理,有效缓解了训练压力并降低了计算量。仿真实验表明,所提出的参数化Wald检测器的检测性能优于之前提出的参数化广义似然比检测器的性能。      

方差均值平方比恒虚警检测器

为了提高雷达恒虚警(Constant False Alarm Rate,CFAR)检测器在多目标背景下的鲁棒性,更好地检测目标,提出了一种新的基于方差均值平方比(VMSR)的恒虚警检测器,并建立了相应的检测器模型,得出了标称化因子T值和置信区间(a,b)。在均匀背景和多目标背景下,对VMSR检测器进行了仿真分析。在均匀背景下,VMSR检测性能优于OS,相比CA仅有很小的检测损失;在多目标背景下,VMSR检测性能相比OS得到了提升,特别是在干扰目标个数r>N-k时,OS不能有效检测出目标,而VMSR仍能保持较好的检测性能。结果表明,VMSR在多目标背景下检测性能优于OS,其在多目标背景下具有较强的鲁棒性。     

基于删除平均和单元平均的CFAR检测器性能分析

基于删除平均(CM)和单元平均(CA),提出了一类恒虚警率(CFAR)检测器,其采用CM和CA产生局部估计,再对局部估计进行平均、选大、选小等逻辑运算实现对杂波功率的估计。分析了所提3种检测器的在均匀和非均匀背景下的检测性能。结果表明,在均匀背景和多目标环境下,对局部估计进行平均的检测器性能最优。     

基于有序数据可变索引的SAR图像目标检测方法

该文提出了一种基于有序数据可变索引(Ordered Data Variability Index, ODVI)的SAR图像目标恒虚警检测算法,该算法首先对待测像素的参考窗进行基于ODVI的自适应筛选处理(Automatic Censoring, AC),以去除窗内的强杂波和干扰像素,并以窗内保留的均匀像素对背景的统计特性进行建模,估计其概率密度函数的参量,同时构建双参数恒虚警检测(CFAR)的检验统计量,计算检测的自适应阈值,实现检测的判决。论文给出了该算法的检测性能曲线,并利用实测的X波段SAR图像进行实验验证,与其它检测方法进行比较,结果显示该文算法具有较好的检测性能和较低的虚警概率。     

一种新的基于剔除平均的最大选择恒虚警检测器

本文基于剔除平均(TM)提出了一种新的最大选择(GO)恒虚警检测器,它的前、后沿滑窗均采用TM来产生局部估计,再选择两者之中的最大值作为检测器对杂波功率水平的估计,去设置自适应检测门限,并应用了何友(1994)提出的自动筛选技术。分析结果表明,它在均匀背景及多目标和杂波边缘引起的非均匀背景中的性能,均比GOSGO或OSGO获得了改善,并且它的样本排序时间还不到OS的一半。一些流行的恒虚警方法如GO、GOSGO或OSGO、CMGO可看作是TMGO的特例。     

一类基于VMSR的恒虚警检测器

设计了一类基于VMSR的改进型恒虚警检测器,给出了检测器的检测结构框图,同时描述了该类检测器的检测模型并给出了相应的检测概率表达式。该类检测器通过VR删除算法自适应删除杂波背景中的干扰样本,并将剩余样本按照不同操作得到杂波背景功率估计,提高了复杂杂波背景下检测器的精确性及鲁棒性。通过与典型VMSR-CFAR检测器的仿真对比实验,验证了该类检测器的检测性能。     

三种Weibull背景下的延展目标的CFAR检测器

针对Weibull杂波背景下延展目标的恒虚警率（CFAR）检测问题,研究了三种积累检测器——二进制积累检测器、非相参积累检测器和秩积累检测器。首先在单脉冲情况下分别从理论上导出了这三种检测器均满足CFAR性以及对应的检测性能解析式,然后将单脉;中条件下CFAR方法推广到多脉7中积累情况,给出了四种可能的处理方案。最后仿真结果验证了理论分析的正确性。