基于删失数据的低通信量融合检测方法

在多基地雷达中,该文为解决局部雷达站同融合中心之间通信带宽受限的问题,提出一种基于删失数据的分布式融合(CDDF)检测算法。在局部雷达站具有多通道接收系统的条件下,计算了杂波背景下动目标回波信号的似然比函数。各个局部雷达站根据其自身传输信道的通信限制设置局部门限,剔除低于局部门限的似然比,同时将高于局部门限的似然比向融合中心传输。基于奈曼-皮尔逊引理,融合中心根据接收到的删失数据计算全局检验统计量,并将其与全局门限进行比较获得全局判决。此外,该文推导了全局门限同虚警概率或者检测概率的闭式表达式。数值仿真表明,该算法可以在大幅降低通信率的同时获得比“或”准则更好的检测性能,并且随着通信率的增加逐渐逼近集中式(CF)融合的检测性能。     

恒虚警极化自适应子空间检测器

本文研究了在未知统计特性的局部均匀高斯杂波环境中的相干雷达极化自适应目标检测问题.基于广义似然比检验提出极化自适应子空间检测器,并推导出虚警概率和检测概率的理论表达式.研究结果证明该算法在均匀杂波环境和局部均匀杂波环境都有恒虚警的性质.比较了均匀杂波环境下极化自适应子空间检测器、极化广义似然比检验和极化自适应匹配滤波器算法的检测性能,仿真分析了不同极化状态局部均匀杂波环境下极化自适应子空间检测器算法在的检测性能.     

极化空时自适应匹配滤波检测器

针对极化空时自适应处理时目标极化状态和杂波协方差矩阵未知等实际瓶颈问题,提出了一种适应于机载极化阵列雷达的极化空时自适应匹配滤波(PST-AMF)检测算法.该检测算法先利用回波数据估计目标的极化状态,然后再将估值代入似然比得到了新的检验统计量,进一步推导了检测器虚警概率和检测概率的解析表达式,从理论上证明了该检测方法具备恒虚警(CFAR)特性.该检测器计算量比极化空时广义似然比检测器(PST-GLRT)少,易于工程实现.最后,仿真验证了在检测慢速运动目标时,其性能优于单个通道的空时自适应匹配滤波检测器(ST-AMF),具备较强的稳健性.     

分布式目标的子空间双门限GLRT CFAR检测

 研究了分布式目标在球不变随机变量杂波中的检测问题,提出了一种具有恒虚警特性的双门限广义似然比检测器。分布式目标建模为子空间信号,在距离维和多普勒频率维同时扩展.第一门限的作用是筛选信杂比高的待检测距离单元.将选出的距离单元进行能量积累并与第二门限进行比较做出判决.假设杂波协方差矩阵已知,构造了双门限检测器,并通过推导检测器虚警概率说明其具有恒虚警特性.将基于辅助通道数据的杂波协方差矩阵的估计值替换假设已知的杂波协方差矩阵,得到一个自适应检测器.通过Monte Carlo仿真进行性能分析,说明检测器的有效性和鲁棒性.     

部分均匀杂波环境中极化自适应Rao检测器

研究了机载极化雷达在部分均匀杂波背景下对于低慢小目标的自适应检测问题。基于Rao准则设计得到了极化自适应Rao检测器,分析了极化自适应Rao检测器的性能,推导得到了虚警概率和检测概率的解析表达式,证明了极化自适应Rao检测器对于杂波协方差矩阵和杂波功率比因子具有恒虚警率特性。理论分析和数值仿真表明,与基于广义似然比检验准则得到的自适应子空间检测器和自适应匹配检测器相比,极化自适应Rao检测器具有更好的检测性能和更低的计算复杂度。      

基于时频融合的分布式目标的恒虚警率检测

从双门限检测理论出发,将时域、频域双门限检测法结合起来,推导出一种基于时频融合的分布式目标的恒虚警率检测方法。以同一目标的两个不同角域的距离像检测为例,通过仿真验证了时频融合检测法具有较好的检测性能,并将其与时域、频域双门限检测法和广义似然比检测法进行了性能比较。结果表明：时频融合检测法优于广义似然比检测法;而与时域、频域双门限检测法相比,时频融合检测法对不同角域距离像具有良好的稳健性。     

基于多通道分段自相关的弱信号检测算法及实现

通过对雷达信号检测方法的深入分析,提出了一种基于多通道分段自相关的方法,成功地解决了低信噪比下信号检测问题。文章采用多通道并行处理,自适应门限,数据融合的方法实现了对不同脉冲宽度的信号检测,并通过双门限检测和门限修正降低虚警率并提高参数估计精度。文章理论分析了整个算法流程,并通过FPGA硬件实现验证,表明该算法实时性好、检测概率高,在工程应用中具有一定的参考价值。     

严重拖尾复合高斯杂波中目标的自适应极化检测

该文研究极化高分辨雷达在动态变化的杂波场景中自适应检测小目标的问题.将统计特性严重拖尾的杂波建模为纹理分量为逆伽马分布的复合高斯过程,借助于广义似然比检验和辅助数据得到了自适应极化检测器,并推导了该检测器的虚警概率表达式,证明了该检测器对协方差矩阵结构具有恒虚警特性.最后,利用仿真杂波数据验证了检测器检测性能的有效性.     

基于Wigner-Hough变换的LFM信号检测性能分析

根据Wigner-Hough变换思想和广义似然比检验理论,分析了双门限情况下线性调频信号的检测性能,给出了虚警概率和检测概率的数学表达式,该表达式是Hough变换的积累单元数和检测门限的函数,然后对概率密度函数进行了合理近似,并给出了Hough变换的积累单元数计算公式,最后的计算机仿真比较了傅立叶变换和Wigner-Hough变换的LFM(Linear Frequency Modulated)信号检测性能和基于Wigner-Hough变换的不同目标起伏类型的LFM信号检测性能.     

分布式CFAR检测系统的最优门限求取新方法

针对并行分布式检测网络结构局部采用CFAR检测器的情形,提出了一种在融合规则给定时,局部CFAR检测器最优门限的求取方法。给出了在任意融合规则下融合中心的检测概率和虚警概率关于局部门限的一阶导数的数值计算公式,并利用Broyden方法求取最优的局部门限。该方法避免了传统方法需要计算二阶导数的复杂性,并且更容易推广到局部采用多类CFAR检测器的情形。实验分析表明了算法的有效性。     

基于局部检测统计量的分布式CFAR检测理论研究

本文系统研究了融合中心接收局部检测统计量的分布式CFAR检测技术。在假设局部检测器背景杂波功率水平相同以及不同传感器测试单元之间服从独立同分布的前提下,首先推导了局部采用单元平均（CA）检测器时融合中心的检测概率与虚警概率的解析表达式,在此基础上利用Laplace变换的频域微分性质,将其推广到局部采用其它检测器,获得了普适性的融合方法,可适用于局部采用任意数量、形式的均值（ML）类和有序统计量（OS）类CFAR检测器时的情形,避免了传统的融合中心采用MOS类方法时所需要的局部信杂比相同的假设,实验结果表明了算法的有效性。      

Autocorrelation-Based Decentralized Sequential Detection of OFDM Signals in Cognitive Radios

This paper introduces a simple and computationally efficient spectrum sensing scheme for Orthogonal Frequency Division Multiplexing (OFDM) based primary user signal using its autocorrelation coefficient. Further, it is shown that the log likelihood ratio test (LLRT) statistic is the maximum likelihood estimate of the autocorrelation coefficient in the low signal-to-noise ratio (SNR) regime. Performance of the local detector is studied for the additive white Gaussian noise (AWGN) and multipath channels using theoretical analysis. Obtained results are verified in simulation. The performance of the local detector in the face of shadowing is studied by simulations. A sequential detection (SD) scheme where many secondary users cooperate to detect the same primary user is proposed. User cooperation provides diversity gains as well as facilitates using simpler local detectors. The sequential detection reduces the delay and the amount of data needed in identification of the underutilized spectrum. The decision statistics from individual detectors are combined at the fusion center (FC). The statistical properties of the decision statistics are established. The performance of the scheme is studied through theory and validated by simulations. A comparison of the SD scheme with the Neyman-Pearson fixed sample size (FSS) test for the same false alarm and missed detection probabilities is also carried out.     

Asymptotic locally optimal detector for large-scale sensor networks under the Poisson regime

We consider distributed detection with a large number of identical binary sensors deployed over a region where the phenomenon of interest (POI) has spatially varying signal strength. Each sensor makes a binary decision based on its own measurement, and the local decision of each sensor is sent to a fusion center using a random access protocol. The fusion center decides whether the event has occurred under a global size constraint in the Neyman-Pearson formulation. Assuming homogeneous Poisson distributed sensors, we show that the distribution of "alarmed" sensors satisfies the local asymptotic normality (LAN). We then derive an asymptotically locally most powerful (ALMP) detector optimized jointly over the fusion form and the local sensor threshold under the Poisson regime. We establish conditions on the spatial signal shape that ensure the existence of the ALMP detector. We show that the ALMP test statistic is a weighted sum of local decisions, the optimal weights being the shape of the spatial signal; the exact value of the signal strength is not required. We also derive the optimal threshold for each sensor. For the case of independent, identically distributed (iid) sensor observations, we show that the counting-based detector is also ALMP under the Poisson regime. The performance of the proposed detector is evaluated through analytic results and Monte Carlo simulations and compared with that of the counting-based detector. The effect of mismatched signal shapes is also investigated.     

授权用户信号随机到达时基于双门限的多用户合作频谱感知方法

针对认知无线电系统中可能出现的授权用户信号随机到达时的频谱感知问题,提出了一种基于双门限的多用户合作频谱感知方法.首先在本地判决时采用高低双门限的方式,如果判决量大于高门限就判1,小于低门限判0,在两个门限之间就把判决量直接发送给融合中心,融合中心将直接收到的判决量进行软判决合并,软判决的结果再与其他硬判决的结果进行"OR原则"的硬判决合并,从而得到最终的判决结果.仿真表明所提方法的性能比传统"OR原则"硬判决合并方案要好,仅仅略逊于传统等增益软判决合并方案,但是能大量节省认知网络中的传输比特数.     

基于FHT似然比检验的CRN协作频谱感知方案

针对认知无线电网络中由于噪声功率不确定性而影响频谱检测性能的问题,提出了一种基于模糊似然比检验的协作频谱检测方法。首先,将每个次用户(SU)中的噪声不确定性均建模为模糊假设检验(FHT);然后,在FHT上利用似然比检验构建带有阈值的模糊能量检测器,该阈值依赖于噪声功率不确定性边界;最后,在融合中心结合SU的局部硬决策并做出最终决策,从而检测主用户是否存在。通过Monte Carlo模拟受试者ROC曲线及检测概率/SNR曲线验证了本文方法的有效性,仿真结果表明,相比其他几种较新的能量检测器,本文方法获得了更好的检测性能。     

距离扩展目标的双门限恒虚警检测器及性能分析

针对普通积累检测器检测稀疏散射点目标存在坍塌损失以及依赖于散射点密度的二元积累(SDD-BI)检测器检测密集散射点目标存在信杂比损失的问题,该文充分利用目标的散射点密度及幅度信息,提出了一种距离扩展目标的双门限恒虚警检测器(DT-CFAR),在高斯杂波背景下推导了DT-CFAR虚警概率和检测概率与检测门限关系的解析表达式,并给出了最优第1门限的确定方法。仿真结果表明,DT-CFAR对不同散射点密度目标都具有较优的检测性能,并且对散射点密度估计失配具有鲁棒性。     

On the decision fusion for cooperative spectrum sensing in cognitive radio networks

Cooperation among different cognitive radios is used to enhance the sensing performance by exploiting the multiuser diversity. Soft and Hard decision fusion schemes are usually employed at the fusion center to detect the presence or absence of primary user. Soft decision fusion schemes perform better than hard fusion schemes but at the cost of increased bandwidth burden on the control channel. In this context, we propose a semi-soft fusion scheme to achieve a tradeoff between sensing performance and bandwidth cost. Under this scheme, each cognitive radio makes a local decision and sends one or two-bit data to the fusion center based on the observed test statistics. On the basis of received data, fusion center estimates the global test statistics which are then compared to a predetermined threshold to make the final decision. Furthermore, a closed form expression for average bandwidth cost for the proposed scheme is also derived. MATLAB simulation demonstrates that the performance of the proposed semi-soft fusion approaches to that of soft fusion scheme with a remarkable reduction in the bandwidth cost of control channel.     

Threshold optimization of decentralized CFAR detection in weibull clutter using genetic algorithms

The use of genetic algorithms (GAs) tool for the solution of distributed constant false alarm rate (CFAR) detection for Weibull clutter statistics is considered. An approximate expression of the probability of detection (P _D) of the ordered statistics CFAR (OS-CFAR) detector in Weibull clutter is derived. Optimal threshold values of distributed maximum likelihood CFAR (ML-CFAR) detectors and distributed OS-CFAR detectors with a known shape parameter of the background statistics are obtained using GA tool. For the distributed ML-CFAR detection, we consider also the case when the shape parameter is unknown of the Weibull distribution. A performance assessment is carried out, and the results are compared and given as a function of the shape parameter and of system parameters.     

基于全参考滑窗的OSTCA恒虚警信号检测器

通过分析分布式检测系统的工作原理,提出了一种基于全参考滑窗观测样本的剔除最大均值-有序(OST-CA-CFAR)检测方法,它对检测单元中的所有杂波样本分别进行有序统计(OS)和剔除最大平均(TCA)处理,再对结果求最大来获得对杂波功率水平的估计,推导了平均虚警概率、平均检测概率的解析表达式,并与其他检测方案进行了对比,分析表明,该方法在多目标和杂波边缘背景情况下能有效提高系统的性能.