一种多目标环境下的SAR图像自适应CFAR检测方法

多数CFAR检测器在多目标检测环境下需要关于干扰目标的先验信息,当检测环境发生变化时,这些检测器很难维持稳定的检测性能。针对多目标环境下的SAR图像目标检测,提出一种新的自适应CFAR（恒虚警）检测器。该检测器利用局部的杂波功率水平估计以及目标和杂波的方差特征筛选出参考窗中的均匀杂波像素,同时剔除掉干扰目标像素;在筛选过程中,每一步使用的判决门限根据上一步的判决结果自动更新;最后对筛选出的样本点作单元平均处理形成检验统计量;完全不需要干扰目标的任何先验信息。利用实测数据仿真研究了该检测器的检测性能与运行效率,实验结果表明,相对单元平均CFAR检测器及有序统计量CFAR检测器,该检测器提高了检测性能,保留了目标精细的结构特征,而运行效率与有序统计量CFAR检测器相当,很具实用性。     

Adaptive detection of range-spread targets in compound Gaussian clutter with the square root of inverse Gaussian texture

The compound Gaussian clutter with the square root of inverse Gaussian texture component has been successfully used for modeling the heavy-tailed non-Gaussian clutter measured by high-resolution radars. In high-resolution radars, the targets may extend along multiple consecutive range cells, which are called range-spread targets. In this paper, we consider the range-spread target detection problem in the compound Gaussian clutter with the square root of inverse Gaussian texture. Three adaptive detectors are proposed based on Bayesian one-step generalized likelihood ratio test, maximum a posteriori generalized likelihood ratio test and Bayesian two-step generalized likelihood ratio test, respectively. Finally, the detection performances of the proposed detectors are evaluated by the Monte Carlo simulation. The simulation results show that the proposed detectors have better detection performance of range-spread target than the conventional generalized likelihood ratio test detector.     

Adaptive subspace detection of range-distributed target in compound-Gaussian clutter

This paper deals with high-resolution radar (HRR) adaptive detection of range-distributed target embedded in compound-Gaussian clutter which is modeled as a spherically invariant random process (SIRP). Using multiple dominant scattering (MDS) model of range-distributed target, we justify that range-distributed target can be modeled as a subspace random signal. The unknown deterministic parameters are replaced by their ML estimates and then the nonadaptive detector is proposed. A closed-form expression for the probability of false alarm of the nonadaptive detector is derived and it ensures CFAR property with respect to the unknown statistics of the clutter texture component. Moreover, an adaptive detector is obtained relying on a two-step GLRT-based design procedure. Performances of these proposed detectors are assessed through Monte Carlo simulations and are shown to have better detection performance compared with existing similar detector.     

Constant false alarm rate detector based on the maximal reference cell

In order to improve the detection performance of constant false alarm rate (CFAR) detectors in multiple targets situations, a CFAR detector based on the maximal reference cell (MRC) named MRC-CFAR is proposed. In MRC-CFAR, a comparison threshold is generated by multiplying the amplitude of MRC by a scaling factor. The number of the reference cells left, whose amplitudes are smaller than the comparison threshold, is counted and compared with a threshold integer. Based on the comparison result, proper reference cells are selected for detection threshold computation. A closed-form analysis for MRC-CFAR in both homogeneous and non-homogeneous environments is presented. The performance of MRC-CFAR is evaluated and compared with other CFAR detectors. MRC-CFAR exhibits a very low CFAR loss in a homogeneous environment and performs robustly during clutter power transitions. In multiple targets situations, MRC-CFAR achieves a much better detection performance than switching CFAR (S-CFAR) and order-statistic CFAR (OS-CFAR). Experiment results from an X-band linear frequency modulated continuous wave radar system are given to demonstrate the efficiency of MRC-CFAR. Because ranking reference cells is not required for MRC-CFAR, the computation load of MRC-CFAR is low; it is easy to implement the detector in radar system in practice.     

一种基于ODV和CA的恒虚警检测器

基于有序数据方差(ODV)方法和单元平均(CA)方法提出一种新的恒虚警检测器(MODVCA),它的前沿和后沿滑窗分别采用ODV和CA产生局部估计,再取二者的和作为背景功率水平估计.在Swerling Ⅱ型目标假没下,推导出MODVCA在均匀背景下虚警概率的解析表达式,并与其他CFAR方法进行了比较,结果表明在均匀背景及多于扰目标情况下,MODVCA的性能均比MOSCA获得了改善,同时该检测器的样本排序时间只有ODV的四分之一.     

基于Stackelberg博弈的多输入多输出雷达信号与目标干扰优化

针对实际杂波环境中多输入多输出(MIMO)雷达与目标间检测与隐身的博弈问题,提出一种新的两步注水算法。首先建立时空编码模型;然后基于互信息量准则,用注水法分配目标干扰功率,用通用注水法分配雷达信号功率;最终得到强弱杂波环境Stackelberg博弈中目标占优和雷达占优的优化方案。仿真结果表明,雷达信号功率分配和通用注水水位变化规律均受杂波影响,两优化方案的互信息量在强杂波环境降低约50%,干扰影响系数分别降低0.2和0.25,互信息量受干扰影响程度降低,证明了所提算法的有效性。     

利用高斯混合模型的SAR图像目标CFAR检测新方法

SAR（合成孔径雷达）图像杂波分布模型种类繁多且对实际地物的建模能力有限。在使用基于杂波统计模型的CFAR(恒虚警率)算法对SAR图像进行目标检测时,杂波统计模型的失配会导致检测结果产生较大的CFAR损失,算法精度不高。提出了一种基于高斯混合模型的CFAR检测新方法。该方法以理论上可以拟合任意形状概率密度分布的高斯混合模型对实际SAR图像的背景杂波进行拟合,利用拟合后得到的分布模型,根据CFAR检测的原理推导出目标检测阈值的计算公式完成目标的检测。新方法对服从不同分布模型的背景杂波,使用形式上统一的模型进行描述,克服了CFAR检测高度依赖背景杂波分布的缺点,提高了CFAR的通用性。实验结果表明,即使在背景杂波类型未知的情况下,新方法依然得到了良好的目标检测效果。     

An adaptive cell averaging-based CFAR detector for interfering targets and clutter-edge situations

In practice, there are two common situations when the independent and identically distributed (IID) assumption no longer holds: (i) there is a clutter edge and (ii) there is an outlier, e.g., a clutter spike, an impulsive interference, or another interfering target. These can result in masking of weaker targets near stronger ones and excessive false alarms at clutter edge transitions. In this paper, a new constant false alarm (CFAR) detector is proposed, which uses a goodness of fit test to verify the IID assumption. If it is decided that the data in the reference window is IID, the cell averaging (CA)-detector is applied. Otherwise, a range-heterogeneous detection algorithm is applied to provide homogeneous samples to develop a CA-based detector. The performance study shows that the proposed detector performs like the CA detector in the homogeneous situation and outperforms other competing CFAR detectors in heterogeneous situations caused by multiple targets and clutter edge.     

一种自适应的合成孔径雷达图像目标检测方法

目标检测是自动目标识别的一个重要步骤,论文提出了一种自适应的SAR图像目标检测方法,该方法采用基于Weibull分布模型的恒虚警率(CFAR)检测技术,将参考窗口分块,判断各子块类型,根据各子块类型不同,自适应选择参考样本确定阈值。在检测过程中,利用灰度和方差特征,预先排除明显不为目标的像素。对CFAR检测结果,利用目标基本形状特征排除虚警。实验证明,该方法在同质区和非同质区背景下都具有较好的检测性能。     

Investigation on dual‐port printed MIMO antenna with reduced RCS for C‐band radar application

In this communication, two port printed Multi Input Multi Output (MIMO) antenna with reduced radar cross section and low mutual coupling is designed and analyzed. Reduced mutual coupling (less than ?20 dB) is achieved by placing the electromagnetic band gap structure in between the two identical ports. Frequency selective surface has been used to reduce the radar cross section (more than 30 dB reduction) of the proposed antenna design, which makes it suitable for military applications. The proposed radiating design is fabricated and measured for the purpose of validation. It resonates at 6.8 GHz supporting an impedance bandwidth of 1.03 GHz from 6.12 to 7.15 GHz. It promises a gain of 4.75 dB in the working frequency range. This radiator is suitable for military radars works in C‐band.     

两种非参量检测器在非瑞利杂波中的检测性能

现代高分辨率雷达系统中,杂波分布已不再简单地服从瑞利分布,其统计特性往往无法预先确定,此时针对性较强的参量检测方法就失去了恒虚警的检测能力,因此鲁棒性较强的非参量检测方法已成为一个重要的研究方向.文中针对非瑞利杂波中广义符号(GS)检测器和Mann-Whitney(MW)检测器两种非参量检测器在两种非瑞利杂波中的检测性能进行了仿真分析.选择韦伯(Weibull)分布和对数正态(log-normal)分布为非瑞利杂波模型,详细给出了仿真模拟框图,采用Monte Carlo仿真方法,分别得出了GS、MW及最佳线性参量检测器在Weibull和log-normal杂波对非起伏目标的检测性能曲线.仿真结果表明,GS和MW在非瑞利杂波中的检测性能均优于最佳线性参量检测器,不同的杂波分布具有相同的均值与中值比(ρ)时,两种检测器性能相差不大.论证了增大独立脉冲积累数(M)是提高检测性能的有效手段.     

机场跑道异物监测雷达的杂波图恒虚警率检测

随天线扫描平稳变化的强地杂波是实现机场跑道异物(FOD)检测的主要干扰,传统的空域恒虚警率(CFAR)处理不能有效地检测到目标,针对上述问题,提出了一种单元平均杂波图恒虚警率检测算法。首先基于系统特性和跑道环境建立了回波信号模型;然后通过杂波图划分、单元平均、递归滤波等处理技术,实现了距离—方位二维恒虚警率检测;最后进一步分析影响检测性能的主要参数。仿真结果表明,所提算法在低信杂比背景下能有效检测到弱目标,并获得较高的检测概率。     

一种改进MVI-CFAR检测器的性能分析

VI-CFAR检测器在均匀环境和非均匀环境下均具有较强的自适应性,但若前后两个参考滑窗均存在干扰目标时,VI-CFAR检测性能急剧下降。为克服该问题,本文采用有序统计平均CFAR和有序统计选小CFAR来替代VI-CFAR中最小选择CFAR。所提出的算法不但解决了干扰目标位置随机分布的问题,还解决了当其中一个滑窗的干扰目标数超过最大容限时检测性能下降的问题。在参考滑窗中干扰目标个数与扰噪比均不相同的情况下,分析了统计量VI的变化,有助于评估和设计VI-CFAR类检测器在多目标环境下抗干扰目标的最大容限。仿真结果表明,该改进算法提高了VI-CFAR在多目标环境下的鲁棒性,与其他改进算法相比,其排序处理时间降低一倍。     

基于MMPHDF的多机动目标联合检测、跟踪与分类算法

针对场景中存在新目标出现、旧目标消失(即目标数目变化)和密集杂波的复杂情形,利用多模型概率假设密度滤波器(MMPHDF)在多机动目标联合检测与跟踪上的优势,加入类别辅助信息,提出了一种多机动目标联合检测、跟踪与分类算法.该算法的基本思想是在MMPHDF中用属性向量扩展单目标状态向量,用位置和属性的组合测量似然函数代替单目标位置及杂波位置测量似然函数,提高了不同类目标与杂波测量间的鉴别能力,从而改善了目标数目及状态的估计精度;在更新目标状态后,对目标属性信息进行更新,更为精确的目标数目及状态估计又保证了目标分类性能.本文给出了该算法的粒子实现方法.仿真结果验证了上述结论.     

一种基于二次Gamma核的SAR图像舰船目标检测方法

针对CFAR检测方法在强海杂波环境中虚警率过高的问题,该文提出了利用二次Gamma核的目标检测方法对SAR图像舰船目标进行检测。该方法可以克服CFAR目标检测方法的缺陷,在强海杂波环境中能有效检测目标,通过实验证明了该方法大大提高了图像的平均信杂比,降低了检测的虚警率。     

一种新的主动声纳模糊均值CFAR检测器

为了改善主动声纳的检测性能,本文研究了非高斯分布混响背景下采用模糊逻辑方法进行恒虚警检测设计的问题,提出了一种二元分布式模糊均值恒虚警检测器,两个子检测单元分别计算映射到虚警空间的隶属函数值,通过融合中心得到全局隶属函数值,实现背景混响功率水平估计,从而进行目标判决.仿真结果表明,基于代数和融合准则的检测器性能是最稳健的,相比传统的二进制“与”逻辑以及“或”逻辑,能够提供更好的检测效果.     

分布式自动删除平均恒虚警率检测技术

根据自动删除平均算法提出了一种新的分布式多传感器的目标检测算法. 在该方法中, 首先根据自动删除平均算法(Censored cell-averaging, CCA)得到各传感器的杂波/噪声电平估计, 然后将检测单元电平与得到的杂波/噪声电平估计值相比较, 得到有无目标的局部判决,并将其传送到融合中心. 融合中心采用"k/N'融合准则得到有无目标的全局判决. 其中, 自动删除平均算法的优势明显, 它不需要干扰的先验信息, 可以容纳的干扰目标数不会像顺序统计量OS (k) (Order statistics)方法那样受指定k值的限制, 更接近实际. 自动删除平均算法还可以检测本身可能是目标的干扰. 在假定目标服从Swerling 2型起伏的情况下, 导出了相应的检测概率与虚警概率解析表达式. 多种检测器数值和图表分析的比较结果表明了该方法的有效性和优越性.     

SAR图像CFAR检测的快速算法综述

针对合成孔径雷达(Synthetic aperture radar, SAR)图像目标检测中恒虚警率(Constant false alarm rate, CFAR)算法的广泛应用, 进行CFAR检测的快速算法分析具有重要研究价值. 首先概述了当前国内外对SAR图像CFAR检测快速算法的研究现状; 然后分别从快速预筛选和迭代计算方法两个方面对各类快速算法的实时性及性能进行了分析总结, 给出了四种基本CFAR检测器的迭代计算公式, 并提出了一种研究CFAR检测快速算法的基本框架, 现有的快速算法均可纳入该理论框架予以分析; 最后, 以经典双参数CFAR检测算法为例, 对该基本框架进行仿真实现和性能分析, 验证了其可行性与检测性能．结果表明: 新的CFAR检测快速算法基本框架充分融合了快速预筛选思想和迭代计算方法的优势, 有效提高了CFAR算法在SAR图像检测应用中的执行效率.     

A polarimetric CFAR ship detector based on the joint probability function of simulated first-order Markov chains

In this work, a novel ship detection procedure is presented for radar signals whose backscattering power is of the same order while their polarimetric characteristics differ greatly. The stochastic nature of the polarimetric characteristics of the background (clutter) and the target signal (ship) can be adequately described by first-order Markov chains (FMCs). The proposed feature corresponds to the joint probability of sequential states of variable finite sequential segments of the FMCs. The classic Constant False Alarm Rate (CFAR) detection theory is adapted to the discrete space of the Markov experimental distributions of the proposed feature while the adaptive thresholding technique is overcome by considering a global model for describing the clutter. Performance assessment of the proposed detection procedure is carried out by means of three sets of FMCs each one corresponding to a clutter-target pair for different lengths of the finite FMC segments. The experimental results present high detection scores making the proposed detection procedure ideal for signals characterized by the Markov property. A comparative study of previous detection approaches has been implemented that shows the superiority of the proposed detection procedure.     

一种基于自适应背景杂波模型的宽幅SAR图像CFAR舰船检测算法

利用海洋宽幅SAR图像进行大范围海域舰船检测在海洋监视、军事侦察等方面具有重要应用。由于海况的复杂性,宽幅SAR图像背景杂波特性随海域不同而变化。采用双参数CFAR检测算法和基于K分布CFAR检测算法在处理宽幅SAR图像时,由于在待检测的所有区域采用同种背景杂波模型,导致使用的杂波模型在不适应区域失配,使CFAR检测性能下降。针对这个问题,提出了一种基于自适应背景杂波模型的CFAR宽幅SAR图像舰船检测算法,该算法通过背景窗口的多尺度统计方差判断目标所处的杂波环境,自适应选择对应的背景杂波分布模型,最后根据已知的恒虚警率及选择的杂波概率密度函数进行CFAR检测。对20多幅宽幅SAR图像进行了试验,实验结果表明:该算法在检测精度上有明显的改善。