软件的单故障模型及故障的测试方法

研究表明,软件中残留的故障一般都是难以测试,这些故障往往都是局部的、检测概率比较“小”的故障,也就是说,和正确的软件相比,这些故障的表现往往只是一个或几个符号发生错误,现提出的软件单故障模型正是基于这个原理。测试方法是以面向故障为基础,将故障的检测分为3个部分:故障语句的执行,故障的激活和故障的传播。为加速测试用例的生成,也同时提出了软件的故障模拟方法。最后,给出了部分实验结果。     

利用预报误差分布拟合实现卫星历史轨道机动检测的方法

轨道机动会引起在轨卫星的轨道异常,是空间态势感知的重点关注对象之一。为此,提出一种从卫星的两行轨道要素编目数据中检测历史机动的方法。通过分析预报误差检测轨道参数的异常编目值,进而获得相应的历史机动信息。对卫星的两行轨道要素编目数据两两预报求差得到预报误差的样本数据后,利用期望最大化算法可以拟合得到以高斯混合模型表达的预报误差的概率分布模型;以此为基础,可以确定利用预报误差检测轨道参数异常编目值的门限;利用轨道机动与异常编目值之间的对应关系,最终实现机动检测。对典型活动卫星的机动检测结果表明,所提方法能够在准确检出历史机动事件的同时保持较低的误检率。     

信源数动态变化的一种稳健超定盲分离算法

针对大多数现有盲分离算法都假定信源数已知,分析了信源数未知且动态变化时的超定盲分离(接收天线数多于信源数)算法,通过奇异值分解在线估计信源个数,并修正自适应神经网络的维数。文章在经典Cichocki-Unbenauen算法中加入动量项,设置可变步长,使分离性能得到较大的改进,同时避免训练陷入局部最小。与文献[1]新提出的ANA算法相比,仿真结果证明本文算法具有更快的收敛速度和更小的稳态误差。     

自适应抵消与谱相减结合的单通道语音消噪算法

飞机、坦克和船舶等封闭座舱内部产生的强机械噪声严重地影响了语音通信系统,为此,提出了自适应噪声抵消与谱相减结合的单通道语音消噪算法,其基本原理是:先进行基于相关检测的自适应消噪处理,其后采用谱相减算法消除残留噪声。仿真结果表明:该算法对引擎噪声的消噪效果明显,并能有效地保持语音清晰度,可应用于机械噪声很强的语音通信系统。     

一类新型混沌动力学系统的自适应控制及同步研究

讨论了一类新型混沌动力学系统的自适应控制及同步问题。利用Lyapunov稳定性理论,得出了该类混沌系统的自适应控制方法;将该类系统作为驱动系统和响应系统,实现了在不同初值且参数未知情况下的自适应同步;进行了数值模拟,验证了相关结论。     

一种新的P2P僵尸网络综合防御系统框架

针对目前缺乏对基于P2P协议僵尸网络通用有效检测防御手段的现状,采用分布式Agent技术,提出了P2P僵尸网络综合防御系统框架,并重点分析了基于分布式Agent的P2P僵尸网络监控检测模块,为P2P僵尸网络的检测防御提出了新的思路。     

参照化流形空间融合学习的敏感特征提取与异常检测方法

针对遥测振动信号冲击强、响应周期短、共振频带宽和小样本等特点导致异类模式识别率低的问题,提出基于参照化流形空间融合学习的敏感特征提取与异常检测方法。采用多尺度分析方法将信号正交无遗漏地分解到各尺度带中,提取多尺度特征构造高维特征集；以相同的正常信号样本结合相同类型的异常样本建立专属参照化模型单元,采用线性流形学习获取各参照化模型单元多尺度流形特征差异,增强异常特征的敏感性。融合各参照化模型单元的投影矩阵对原始特征集进行升维再学习,获取低维多尺度敏感流形特征；输入到分类器实现对未知样本状态辨识。实测信号处理结果验证了算法的有效性。     

The prediction formula and a risk-based sensor scheduling method in target detection with guiding information

Sensor scheduling is used to improve the sensing performance in the estimation of targets' states. However, few papers are on the sensor scheduling for target detection with guiding information. This letter can remedy this deficiency. A risk-based target detection method with guiding information is provided firstly, based on which, the sensor scheduling approach is aiming at reducing the risk and uncertainty in target detection, namely risk-based sensor scheduling method. What should be stressed is that the Prediction Formula in sensor scheduling is proposed. Lastly, some examples are conducted to stress the effectiveness of this proposed method.     

Arbitrary-oriented target detection in large scene sar images

Target detection in the field of synthetic aperture radar (SAR) has attracted considerable attention of researchers in national defense technology worldwide, owing to its unique advantages like high resolution and large scene image acquisition capabilities of SAR. However, due to strong speckle noise and low signal-to-noise ratio, it is difficult to extract representative features of target from SAR images, which greatly inhibits the effectiveness of traditional methods. In order to address the above problems, a framework called contextual rotation region-based convolutional neural network (RCNN) with multilayer fusion is proposed in this paper. Specifically, aimed to enable RCNN to perform target detection in large scene SAR images efficiently, maximum sliding strategy is applied to crop the large scene image into a series of sub-images before RCNN. Instead of using the highest-layer output for proposal generation and target detection, fusion feature maps with high resolution and rich semantic information are constructed by multilayer fusion strategy. Then, we put forwards rotation anchors to predict the minimum circumscribed rectangle of targets to reduce redundant detection region. Furthermore, shadow areas serve as contextual features to provide extraneous information for the detector identify and locate targets accurately. Experimental results on the simulated large scene SAR image dataset show that the proposed method achieves a satisfactory performance in large scene SAR target detection.     

卫星通信干扰信号的检测方法

卫星通信系统较易受到各种干扰，尤其是人为干扰，为了抑制干扰，需对信号进行正确的干扰检测。就几种常见的卫星干扰信号，对其进行 Matlab仿真实验，研究不同干扰类型信号的特点，提出一种改进的CME算法用于检测干扰信号是否存在。与能量检测法和传统的CME方法相比，它具有较好的检测性能。仿真结果表明，提出的CME方法可以有效用于干扰检测。