Weibull杂波背景下的双参数CFAR检测器

针对单参数CFAR检测器在Weibull杂波下需事先估计杂波的形状参数,而双参数CFAR检测器如Log-t CFAR等则存在虚警损失较大的问题,基于有序统计（OS）CFAR检测器,结合反馈控制理论,提出了一种适用于Weibull杂波模型的双参数CFAR检测器.理论分析与仿真表明,该检测器无论在均匀背景还是非均匀背景下都具有较好的检测性能.     

一种与杂波类型无关的延展目标CFAR检测器

针对延展目标检测问题,提出了一种与杂波分布类型无关的CFAR检测器.首先从理论上分析了该处理方法的CFAR特性,导出了相应的检测性能解析式.同时,为了证实提出方法的有效性,将其与基于模糊积累的CFAR检测器和二进制积累CFAR检测器进行了实验比较.实验结果表明,该处理方法具有良好的通用性、实用性和稳健性.     

基于多杂波分布模型的自适应单元平均恒虚警检测

针对机载预警（AEW）雷达多杂波分布模型的恒虚警检测问题，提出一种自适应单元平均恒虚警（ACA-CFAR）检测方法．这种检测方法是在经典CA-CFAR检测方法基础上加入自适应判断选择背景参考子窗的一种CFAR处理方法．它是根据杂波功率水平的变化选择背景杂波单元，在此基础上结合CA-CFAR检测方法实现检测．仿真结果表明，ACA-CFAR恒虚警检测方法在均匀背景和多目标背景下均具有较好的检测性能，并且运算量小．     

基于模糊积累的宽带雷达CFAR检测

采用传统的统计检测方法进行高分辨雷达目标检测时不能充分利用目标回波的全部能量,影响雷达的检测性能.基于二进制积累检测方法和模糊检测理论,提出了两种模糊积累的检测方法,从理论上对所提方法的检测性能进行了分析;在均匀杂波背景和多目标背景、杂波边缘背景下进行了性能仿真.理论分析和仿真结果表明:模糊积积累检测方法检测性能比二进制积累检测方法的检测性能有明显改善,是一种稳健的检测CFAR方法.     

一种非均匀环境下多通道SAR动目标检测算法

针对非均匀环境中SAR-GMTI动目标检测性能下降的问题,提出了一种新的动目标检测算法。该算法首先通过广义内积（GIP）来修正杂波的协方差矩阵,从而达到抑制均匀杂波的目的,然后利用恒虚警（CFAR）检测技术提取动目标及强干扰目标,最后根据杂波抑制剩余信号的多普勒中心来剔除强干扰目标。理论分析及实验结果表明,该方法不仅能够很好地抑制均匀杂波,且能剔除强干扰目标,适用于强干扰存在的非均匀环境。     

二维自适应单元平均恒虚警率检测

针对机载预警雷达当杂波背景中出现多个目标或者强干扰时，一般的CA-CFAR检测器和OS-CFAR检测器的检测性能严重下降问题，提出了一种二维情况下的自适应单元平均恒虚警检测方法（2D-ACA—CFAR）．它是通过把超过删除阈值的强干扰从采样集合中删除掉，然后进行CA-CFAR检测．仿真结果表明这种方法在多干扰、强干扰的情况下具有良好的检测性能．     

一种快速排序筛选SAR图像目标CFAR检测算法

传统SAR图像目标CFAR检测算法通常针对低分辨率图像,目标在高分辨率图像中表现为扩展目标时难以获得较好的检测性能.为解决高分辨率SAR图像的目标检测问题,借鉴3种传统CFAR检测算法,研究了一种快速排序筛选SAR图像目标CFAR检测算法.该算法引入杂波像素排序筛选机制,通过获取候选目标区域减少CFAR检测像素点,针对滑窗移动时杂波像素大量重合进行参数快速估计.实验结果表明,该算法与传统CFAR算法相比,在检测效果和检测效率上都有显著提升;而SAR图像的检测性能与筛选深度有关.     

基于统计量ODV的自适应TM-CFAR检测

基于有序数据变率(ODV)和削减平均恒虚警(TM-CFAR)检测,提出了自适应TM-CFAR检测,它能判决自动选择参数并估计背景噪声,仿真结果表明,在均匀背景和多目标背景下,其具有较好的检测性能,能提高抗干扰目标最大容限;在强杂波边缘时,其虚警概率控制能力优于有序统计CFAR检测和单元平均CFAR检测.采用两级结构和分块并行处理思想实现时,该算法所需硬件资源和运算复杂度都低于自动删除平均ODV检测,而且具有实时处理性高和时序控制方便的优点.     

恒虚警问题研究

首先讨论了高斯杂波背景中雷达目标恒虚警检测的原理,然后通过分析慢门限CFAR、邻近单元平均恒虚警（CA—CFAR）检测性能的优劣,提出了改进型CFAR方案,并在计算机模拟统计的基础上对5种处理器的检测性能进行了分析比较,得出对数平均选大恒虚警电路在复杂杂波环境中工作最优,其它各有优越性。     

严重拖尾杂波中距离扩展目标的自适应极化检测

为了解决统计特性未知的严重拖尾杂波背景下距离扩展目标的信号检测问题,提出了一种基于广义似然比检验(GLRT)的自适应极化检测器．该检测器利用了雷达回波的极化信息,并使用辅助数据估计杂波的协方差矩阵．推导了其虚警概率表达式,理论分析验证了该检测器对于杂波能量和杂波协方差矩阵具有恒虚警特性．仿真结果表明,该自适应极化检测器在较低信杂比下就可以获得好的检测性能,且相比于点目标自适应极化检测器和单极化自适应检测器,具有更优的检测性能．     

两种检波方式CA—CFAR检测器性能分析

为了更全面地分析CFAR检测器的性能，对线性和平方律两种检波CA—CFAR检测器检测性能优劣问题进行了研究．首先从理论上分别导出了两种检波的最优检测解析式和在CA—CFAR处理时的CFAR损失结果，然后对两种检波方式的检测性能和CFAR损失进行了全面的比较，得到了相应的结论．     

A Mo LC+Mo M-based G~0 distribution parameter estimation method with application to synthetic aperture radar target detection

The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate(CFAR) target detection method. G0 distribution is one of the optimal statistic models in the synthetic aperture radar(SAR) image background clutter modeling and can accurately model various complex background clutters in the SAR images. But the application of the distribution is greatly limited by its disadvantages that the parameter estimation is complex and the local detection threshold is difficult to be obtained. In order to solve the above-mentioned problems, an synthetic aperture radar CFAR target detection method using the logarithmic cumulant(Mo LC) + method of moment(Mo M)-based G0 distribution clutter model is proposed. In the method, G0 distribution is used for modeling the background clutters, a new Mo LC+Mo M-based parameter estimation method coupled with a fast iterative algorithm is used for estimating the parameters of G0 distribution and an exquisite dichotomy method is used for obtaining the local detection threshold of CFAR detection, which greatly improves the computational efficiency, detection performance and environmental adaptability of CFAR detection. Experimental results show that the proposed SAR CFAR target detection method has good target detection performance in various complex background clutter environments.     

机载预警雷达STAP-TBD技术研究

针对机载预警雷达在强地/海杂波环境下的微弱目标检测问题,研究了基于动态规划的空时自适应处理级联检测前跟踪(STAP-TBD)技术.首先建立了空时二维杂波数学模型并给出了STAP-TBD广义似然比检测模型,通过STAP处理获得单帧最优输出信杂噪比,并运用动态规划法对连续帧数据进行积累,最后利用广义似然比检测完成对目标的有效检测.仿真结果表明,STAP-TBD技术具有在强杂波背景下检测微弱目标的能力.     

Detection of the airborne MIMO radar moving target in the non-Gaussian clutter

Due to the moving platforms, the clutters in distributed airborne MIMO radar are non-Gaussian and non-homogeneous, which leads to having no independent and identically distributed training data to estimate the clutter covariance matrix. To solve the problem, we propose that the covariance of the clutter should be modeled as an inverse complex Wishart distribution whose average value is a Hadamard product of the covariance matrix taper (CMT) and the clutter Doppler spectrum component. Based on this clutter model, a novel detector combing the Bayesian approach and the generalized likelihood ratio test(GLRT) is proposed. Numerical simulation results show that the proposed detector has a better detection performance compared with two current commonly used non-Bayesian detectors.     

Knowledge-based adaptive detection of radar targets in sea clutter background

This paper focuses on the problem of radar targets detection in the compound-Gaussian sea clutter on the condition with the limited secondary data.The texture is modeled by the generalized inverse Gaussian distribution.Two adaptive detectors based on a priori knowledge of the speckle covariance matrix are proposed.First,the inverse complex Wishart distribution is exploited to model the speckle covariance matrix,and then an adaptive detector without using the secondary data is designed according to the generalized likelihood ratio.According to the maximum posterior test criterion,the secondary data are used to design an adaptive detector with secondary data and prior knowledge.Experimental results show that when the number of secondary cells is small,the two detectors proposed in this paper have a better detection performance than the GLRT-GIG detector.With different numbers of secondary cells,the proposed adaptive detector depending on the secondary data and a prior knowledge has the best performance.