高斯背景下距离扩展目标的恒虚警率检测

对宽带高分辨率雷达(HRR)中的距离扩展目标进行检测,若还采用分辨雷达的检测方案,目标回波的全部能量得不到充分利用,会影响雷达的检测性能。提出了一类基于二进制积累的距离扩展目标检测算法,推导出了它们的平均虚警概率和平均检测概率的数学解析式,并进行了性能分析和比较。这些方法较常规低分辨雷达方法可获得目标信噪比8个多分贝的性能改善,且这些次优积累的高分辨雷达距离扩展目标检测方法具有易于工程实现的优点。     

高斯杂波中距离扩展目标的模糊CFAR检测

为提高高分辨雷达的检测性能,提出了一种高斯杂波背景下距离扩展目标的模糊检测方法。该方法采用模糊恒虚警率(constant false alarm rate, CFAR)检测器代替传统的二进制CFAR检测器,将距离单元值转换成映射到虚警空间的模糊隶属函数值,然后采用模糊积累准则进行积累,得到检测统计量。研究了模糊代数积、模糊代数和两种积累准则,推导出这两种方法虚警概率的数学解析式。仿真结果表明,高斯杂波背景下模糊代数积积累较双门限二进制积累可获得近2 dB的性能改善。同时,模糊检测方法只采用单个检测门限,具有易于调节的优点。     

基于距离单元选择的距离扩展目标检测器

针对距离扩展目标所占据的各距离单元输出的信杂比在统计意义上不同的一般情况,在分析基于顺序统计的广义似然比检测(generalized likelihood ratio test based on order statistics, OS-GLRT)的基础上提出了两种改进的检测器。第一种是修正的OS-GLRT,它采用或规则,将所有可能的有效距离单元数量得到的OS-GLRT的结果进行融合后做出判决,所得到的修正的OS-GLRT无需有效距离单元数量等目标先验信息。第二种是结合双门限(dual threshold, DH)技术,通过设置固定的第一门限对有效距离单元数目进行估计,动态的第二门限形成最终的判决。与同类检测器相比,新检测器不需要距离扩展目标的先验信息,具有较好的鲁棒性。     

基于修正熵的距离扩展目标检测器

针对采用球不变随机向量(spherically invariant random vector, SIRV)建模的非高斯杂波背景下，目标散射点个数估计失配时，修正的依赖于散射点密度的检测器（modified scatter density dependent, MSDD)检测距离扩展目标存在一定的信噪比损失问题，提出了一种基于修正熵的MSDD检测器（modified entropy-MSDD, ME-MSDD）。ME-MSDD先采用修正熵对目标强散射点个数进行估计，然后对目标强散射点进行积累形成检测统计量。最后，针对不同的目标模型及实测目标一维距离像对检测器性能进行评估，结果表明，与不依赖于散射点密度的检测器（non-scatter density dependent, NSDD)、基于动态阈值的检测器（dynamic threshold, DT)和MSDD相比，ME-MSDD能根据实际观测值自适应地估计目标散射点个数，具有更好的检测性能和鲁棒性。     

扩展目标的最优二进制检测

研究了高斯白噪声背景下扩展目标二进制检测器的优化问题。散射中心展布特性是制约二进制检测器检测性能的重要因素之一。首先从理论上系统分析了不同的散射中心展布对二进制检测器检测性能的影响,然后采用经验拟合的办法给出了设计最优二进制参数的经验公式,进而得到最优二进制参数设计的一般原则。仿真结果证明,最优二进制参数和距离像的长度及散射中心的个数有关。     

基于广义似然比的宽带分布式目标检测算法

针对在相干积累期间发生距离走动的分布式目标检测问题,提出了一种广义似然算法及其改进算法。该算法采用广义似然函数准则与极大似然方法对不同预设速度下各个距离单元的信号进行估计,得到了两步广义似然比检测算子。进一步利用目标相邻强散射单元特性和相邻预设速度相关性,推导出改进的三步广义似然算法,随后证明了这两种算法的恒虚警率性。对两类飞机实测数据的多个实验结果表明,当目标发生距离走动的情况下,该算法性能相对与传统的非相干积累算法有明显提高。     

海杂波下距离扩展目标的最优二进制检测

研究了低擦地角下高分辨雷达对静止或慢速运动的距离扩展目标的最优二进制检测问题。采用K分布模型描述低擦地角下海杂波的统计分布,采用Rayleigh快起伏模型描述扩展目标距离像的幅度分布,详细分析了扩展目标二进制检测器参数与扩展目标径向长度、杂波形状参数之间的关系,最终得到最优二进制检测器的参数选择准则和经验公式,对二进制检测器的实际应用具有较强的指导意义。理论和实测杂波数据证明了最优二进制检测器的有效性。     

基于Gamma分布的优化SAR目标检测

在Gamma分布条件下,研究了SAR目标检测,得到了阈值系数与恒虚警关系,提出了阈值选择理论及其简单实现方法,优化了杂波均值估计并给出了其无偏鲁棒算法,这样在目标检测中不经过噪声抑制过程但具有抗噪能力,并且参数估计没有偏差。实际SAR数据测试结果表明该方法的检测效果优于双参数恒虚警算法。     

宽带雷达目标的稳健二进制检测算法

由于宽带雷达的高分辨距离像(high resolution range profile, HRRP)对方位变化非常敏感,传统的基于散射点密度广义似然比(scatter density dependent generalized likelihood rate test, SDD GLRT)或二进制积累器(binary integration, BI)检测算法存在参数选择困难的问题。提出的稳健二进制积累器(robust binary integration, RBI)采用高低双门限，分别对HRRP进行量化，然后将过高门限的距离单元数加权后与过低门限的距离单元数求和，并将该值与第二门限比较做出最终判决。而后研究了RBI的虚警概率和参数选择的方法。实测数据仿真结果表明, RBI对高低熵值的HRRP都可保证稳健、良好的检测性能。     

基于知识的SAR图像目标检测

提出了一种基于知识的SAR图像目标检测算法。针对军用车辆,利用各种先验知识,以地形类型信息、距边界的距离信息、目标聚集程度为影响目标出现概率的因素,通过分类获得SAR图像的地形及边缘信息,得到影响因子,并综合地形信息使用MAP准则,从而获得目标检测的结果。使用真实SAR图像进行了测试,结果表明,与CFAR检测算法相比,该算法有效地提高了目标的检测率,虚警目标数目明显减少。     

New CFAR target detector for SAR images based on kernel density estimation and mean square error distance

A new constant false alarm rate (CFAR) target detector for synthetic aperture radar (SAR) images is developed. For each pixel under test, both the local probability density function (PDF) of the pixel and the clutter PDF in the reference window are estimated by the non-parametric density estimation. The target detector is defined as the mean square error (MSE) distance between the two PDFs. The CFAR detection in SAR images having multiplicative noise is achieved by adaptive kernel bandwidth proportional to the clutter level. In addition, for obtaining a threshold with respect to a given probability of false alarm (PFA), an unsupervised null distribution fitting method with outlier rejection is proposed. The effectiveness of the proposed target detector is demonstrated by the experiment result using the RADATSAT-2 SAR image.     

Robust detector for range-spread targets in non-Gaussian background

Based on the target scatterer density,the range-spread target detection of high-resolution radar is addressed in additive non-Gaussian clutter,which is modeled as a spherically invariant random vector.Firstly,for sparse scatterer density,the detection of target scatterer in each range cell is derived,and then an M/K detector is proposed to detect the whole range-spread target.Secondly,an integrating detector is devised to detect a range-spread target with dense scatterer density.Finally,to make the best of the advantages of M/K detector and integrating detector,a robust detector based on scatterer density(DBSD) is designed,which can reduce the probable collapsing loss or quantization error effectively.Moreover,the density decision factor of DBSD is also determined.The formula of the false alarm probability is derived for DBSD.It is proved that the DBSD ensures a constant false alarm rate property.Furthermore,the computational results indicate that the DBSD is robust to different clutter one-lag correlations and target scatterer densities.It is also shown that the DBSD outperforms the existing scatterer-density-dependent detector.     

Switching variability index based multiple strategy CFAR detector

A switching variability index （SVl） constant false alarm rate （CFAR） detector is proposed for improving the detection performance of VI-CFAR detectors in multiple targets backgrounds. When the presence of non-homogeneity in CFAR reference windows is indicated by a VI-CFAR detector, a switching- CFAR detector is introduced to optimize the performance of the VI-CFAR detector in homogeneous, multiple targets and clutter edge backgrounds. The structure and parameters selection method of the SVI-CFAR detector is presented. Comparisons with classic CFAR detectors and recently proposed detectors are also given. Theoretical analysis and simulation results show that SVICFAR detector maintains the good performance of the VI-CFAR detector in homogeneous and clutter edge backgrounds, while greatly improving the capacity of anti-multi targets.     

低复杂度的DSSS信号扩频序列估计算法

在非协作条件下直接序列扩频(DSSS)信号的扩频序列估计是盲解扩的必需环节。通过构造基于接收信号自相关矩阵的两个检验统计量,分别完成了扩频序列的同步和扩频序列的估计。所提的算法具有很低的运算复杂度。仿真实验验证了所提算法性能的有效性。综合算法的鲁棒性、计算复杂度和低信噪比下的估计性能等评价因素,算法相比其它的典型算法有着一定程度的优越性,并易于快速实现。     

New targets number estimation method under colored noise background

A multiple targets detection method based on spatial smoothing (MTDSS) is proposed to solve the problem of the source number estimation under the colored noise background. The forward and backward smoothing based on auxiliary vectors which are received data on some specific elements is computed. By the spatial smoothing with auxiliary vectors, the correlated signals are decorrelated, and the colored noise is partially alleviated. The correlation matrix formed from the cross correlations between subarray data and auxiliary vectors is computed. By exploring the second-order statistics property of the covariance matrix, a threshold based on Gerschgorin radii of the smoothing correlation matrix is set to estimate the number of sources. Simulations and experimental results validate that MTDSS has an effective performance under the condition of the colored noise background and coherent sources, and MTDSS is robust with the correlated factor of signals and noise.     

直接扩频序列信号的参数估计

在频谱监测、电子侦听等非协作通信中,接收机要对直接扩频序列信号进行侦收,必须要首先估计信号的参数,包括波特率、码片速率和载波频率等。由于噪声功率电平远高于接收到的信号功率电平,常规的基于能量和谱的参数估计方法在此并不适用。针对这些问题提出了一种估计算法,该算法首先对接收信号的自相关函数进行累积以消除噪声的影响,然后在此基础上估计信号的波特率、码片速率和载波频率。所有这些算法都不需要知道DSSS信号的具体调制类型。计算机仿真结果表明了这些算法的有效性。     

基于Duffing振子的直接序列扩频信号检测及参数估计

针对直接序列扩频信号检测中信号特征提取的问题,提出了利用Duffing振子对小信号的敏感性和对噪声的免疫性来检测信号的这些特征信息。以基于Duffing振子的载波检测为例,进行了分析和仿真,给出了直接序列扩频信号的幅度、相位和载频的估计值。仿真表明,所提的方法可以获得很好的检测性能,能检测到信噪比为-38.35 dB的DSSS/BPSK信号。