计算机处理检测概率的一种数学模型

论述一种在单个脉冲条件下，雷达的虚警概率、检测概率和信噪比之间的数学关系，并建立起适合计算机编程的数学模型，以便于在计算机程序设计中使用。     

关于雷达虚警及检测性能的研究

从基本雷达方程的推导入手,给出了雷达作用距离和参数之间的定量关系。然后根据在雷达信号检测中应用广泛的奈曼皮尔逊准则,即要求在给定的信噪比条件下,满足一定的虚警概率Pfa时的发现概率Pd最大,通过对噪声的概率密度的研究,画出了曲线图。最后具体用一个例子分析说明了影响检测概率的主要因素是信噪比。     

一种基于信噪比的动态门限协作感知方法

在协作频谱检测中,针对单用户进行信号检测时所处环境的不同会对联合检测性能产生很大影响。为此,根据认知节点不同的信噪比,提出一种可以动态调整判决门限的协作检测方法。仿真结果表明,在联合虚警概率一定的情况下,该方法可明显提高联合检测概率及频谱检测的可靠性。     

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.     

基于自动删除算法的分布式CFAR检测算法

研究分布式恒虚警(CFAR)检测系统在非均匀干扰背景中进行优化检测.针对多传感器分布式恒虚警检测系统在非均匀干扰背景中容易出现检测概率下降或者虚警率提高的问题,提出了一种基于自动删除算法的分布式恒虚警检测算法.算法是一种基于局部检测统计量的分布式CFAR检测算法,充分利用了局部检测器的观测信息,提高了检测性能,同时采用...     

基于CASH-CFAR的跳频信号检测

为解决复杂短波环境下跳频信号检测概率低的问题,提出了一种基于恒虚警概率(CFAR)的稳健的跳频信号检测算法.分析了CASH-CFAR算法,为了提高运算速度对其进行了一定的改进,根据跳频信号的特点将其扩展到二维并应用到时频谱图上检测跳频信号,通过形态学图像处理方法滤除噪声.仿真实验结果表明,该方法能够在低信噪比情况下有效地检测跳频信号,并同时抑制定频干扰,检测率较高,运算速度快.     

基于DS/CDMA系统前缀码捕获性能分析

介绍了在DS/CDMA系统中采用巴克码作为前缀码对捕获性能的影响,提出了基于匹配滤波器前缀码捕获的改进方法。它不仅考虑了频偏和信息数据调制对捕获的影响,还同时实现了码同步和帧同步。用检测概率和虚警概率作为系统性能衡量指标,通过仿真实验证明该算法在前缀码快速可靠捕获方面是有效的。     

噪声响应相关性对CA-CFAR检测器的影响

主要研究噪声响应之间的相关性对CA-CFAR检测器的虚警概率的影响。讨论了加性高斯白噪声环境中噪声响应的统计特性,推导了噪声响应相关情况下CA-CFAR检测器的虚警概率,与噪声响应相互独立的假设条件下的CA-CFAR检测器的虚警概率进行了比较。在上述情况下,对CA-CFAR检测器的检测性能进行了仿真。仿真结果表明在实际工程中可以忽略噪声响应的相关性。     

基于相关多天线的频谱感知研究

在认知无线电中,研究了多天线认知节点运用能量检测法进行频谱感知时,天线相关性对能获得的实际性能增益的影响。详细推导了认知节点配备相关多天线下的检测概率和虚警概率,并分析得到多天线相关时虚警概率的上下界。理论推导证明,认知节点接收信号SNR较低时,相关多天线能达到独立多天线近似的实际性能增益。数值仿真结果与理论分析结果一致,证明了理论分析的正确性。     

基于循环平稳检测的WCSN频谱感知算法

在无线认知传感器网络中,感知节点采用能量检测法在低信噪比下检测概率较低,而传统循环平稳检测法复杂度过高。为此,采用一种基于频域简化的单循环平稳检测算法,推导算法虚警概率的闭合表达式,建立检测概率与实际信噪比之间的关系式。理论研究与仿真分析表明,该算法复杂度低于循环平稳检测法,与能量检测法持平,可以满足无线认知传感器网络中对频谱感知的性能要求。     

基于SAR图像目标检测的优化CFAR检测器算法研究

在目前的SAR目标检测中CFAR检测器被广泛的应用,而对相同或不同分辨率的图像,优化的CFAR检测器可以作为无偏检测。本文对优化的CFAR．检测器进行了研究。     

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。     

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.     

Matrix CFAR detectors based on symmetrized Kullback–Leibler and total Kullback–Leibler divergences

Target detection in clutter is a fundamental problem in radar signal processing. When the received radar signal contains only few pulses, it is difficult to achieve a satisfactory performance using the traditional detection algorithm. In recent times, a generalized constant false alarm rate (CFAR) detector on the Riemannian manifold of Hermitian positive-definite (HPD) matrix was proposed. The employment of this detector, which compares the Riemannian distance between the covariance matrix of the cell under test (CUT) and an average matrix of reference cells with a given threshold, has significantly improved the detection performance. However, the application of this detector in real scenarios is still limited by two problems; it is computationally expensive and the detection performance is not very good since the Riemannian distance is utilized. In this paper, the symmetrized Kullback–Leibler (sKL) and the total Kullback–Leibler (tKL) divergences, instead of the Riemannian distance, are used as dissimilarity measures in the matrix CFAR detector. According to sKL and tKL divergences, three average matrices, the sKL mean, the sKL median, and the tKL t center, are derived. Furthermore, the relationship between the detection performance and the anisotropy of the distance measure used in the matrix CFAR detector is explored. Numerical experiments and real radar sea clutter data are given to confirm the superiority of the proposed algorithms in terms of the computational complexity and the detection performance.     

遗传算法在分布式OS-CFAR检测系统优化中的应用

针对一个3传感器分布式OS-CFAR检测系统，本文分别使用了基本遗传算法和改进的遗传模拟退火算法、小生境遗传算法进行优化搜索，给出了一组不同检测条件下的准最优搜索结果。结果表明，对于这一优化问题，遗传模拟退火算法和小生境遗传算法都具有较好的适应性，其中小生境遗传算法在搜索质量、稳定性和搜索速度上相对更好一些，是一种较理想的多传感器分布式OS-CFAR检测系统参数优化算法。     

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.     

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.     

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。     

Distributed fault detection over sensor networks with Markovian switching topologies

This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via a communication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filters is derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.