计数序贯抽样检验方案的OC函数和ASN

根据序贯概率比检验的理论,给出计数序贯抽样检验方案的设计原理,得到国家标准GB／T8051—2008中计数序贯抽样检验方案的OC函数和ASN的计算方法。     

Limiting behavior of the stochastic sequential assignment problem

The stochastic sequential assignment problem (SSAP) considers how to allocate available distinct workers to sequentially arriving tasks with stochastic parameters such that the expected total reward obtained from the sequential assignments is maximized. Implementing the optimal assignment policy for the SSAP involves calculating a new set of breakpoints upon the arrival of each task (i.e., for every time period), which is impractical for large‐scale problems. This article studies two problems that are concerned with obtaining stationary policies, which achieve the optimal expected reward per task as the number of tasks approaches infinity. The first problem considers independent and identically distributed (IID) tasks with a known distribution function, whereas in the second problem tasks are derived from r different unobservable distributions governed by an ergodic Markov chain. The convergence rate of the expected reward per task to the optimal value is also obtained for both problems. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

有源诱饵的极化鉴别研究

针对全极化测量体制雷达,研究了有源诱饵等欺骗性电子干扰的鉴别问题。首先详细分析了干扰机天线为圆极化和线极化两种典型情况下有源诱饵的极化特性,探讨了雷达目标的极化散射特性;而后提出了能够有效表征有源诱饵和雷达目标极化特性之间差异的参量———共交极化比及其峰值、聚类中心等概念和定义,并设计了有源诱饵的极化鉴别算法;最后,结合实测数据,计算机仿真实验表明鉴别算法的可行性和有效性。     

初相未知信号联合检测与估计的序贯实现

在未知参数的充分统计量和再生概率密度函数的基础上,引入了联合检测与估计的序贯算法,给出了算法的实现框图,并将该算法应用于高斯噪声中初相位未知信号的联合检测与估计当中.仿真实验表明,新算法较传统算法有一定的性能改善.     

ARUAV前沿判别法抗多点源诱偏性能研究

基于反辐射无人机的时域鉴别技术,提出了一种以小波分析为手段的前沿判别法。将被动雷达导引头使用固定波门对脉冲前沿进行采样所得的信号作小波分析,对分析结果进行判定,当判定采样信号内为单辐射源信号时对其进行测向、测频、跟踪和打击。针对两点源诱偏系统分析表明,在大部分空域中,反辐射无人机总是能首先跟踪诱饵信号。通过建模与仿真,验证了前沿判别法能够有效对抗两点源诱偏系统,并由固定波门得到了有效的判别空域。     

A sequential Bayesian generalization of the Jelinski–Moranda software reliability model

The Jelinski–Moranda model of software reliability is generalized by introducing a negative‐binomial prior distribution for the number of faults remaining, together with a Gamma distribution for the rate at which each fault is exposed. This model is well suited to sequential use, where a sequence of reliability forecasts is made in the process of testing or using the software. We also investigate replacing the Gamma distribution with a worst‐case assumption about failure rates (the worst‐case failure rate in models such as this is not infinite, since faults with large failure rates are immediately discovered and removed). © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

一种基于加权DSmT的序列图像目标识别方法

通过对自动目标识别系统研究与分析,将DSmT引入到飞机序列图像识别中,构建基本置信指派及分类规则,提出了一种基于加权DSmT的序列图像目标识别方法,在分析权值选择特点的基础上,根据特征信息的置信指派组合有效权值,并且给出了基于加权DSmT融合规则的序列图像目标识别的流程,仿真结果表明,该方法提高了序列图像目标识别的准确性.     

上下文感知的深度弱监督图像哈希表示学习方法

针对现有深度监督图像哈希表示学习方法依赖于图像的类别信息,难以在现实中被广泛应用问题,利用与图像相关的标签信息作为监督信息,提出上下文感知的深度弱监督图像哈希表示学习方法。该方法一方面通过自适应捕获图像区域特征的相关上下文来增强它们的表示能力,另一方面通过引入判别损失来提高学习到的哈希码表示的判别性。在现有两个公开数据集上的大量实验结果证明了该方法的有效性。     

基于方位特征序列的地雷鉴别算法

在使用低频超宽带合成孔径雷达(UWB-SAR)对地雷进行探测的过程中,根据目标电磁散射随方位角和入射角的变化特性,提出一种利用双峰间距和频率凹点特征沿方位向变化的隐马尔科夫模型(HMM)鉴别算法。该算法首先针对目标感兴趣区域(ROI)图像估计其各方位回波响应,然后利用时频原子提取时域双峰间距和频率凹点,进而得到随方位角变化的特征序列,再通过SAR工作时方位角和入射角的变化特点以及训练样本确定HMM参数,并在此基础上计算疑似目标新的特征矢量,采用马氏距离进行判别。实验结果表明了本文所提方法在目标鉴别方面的有效性。     

一种新的高分辨率SAR图像目标自动鉴别方法

目标鉴别是SAR图像目标识别系统的关键环节,用以消除预筛选阶段因异常检测产生的大量虚假的感兴趣区域切片。针对目标鉴别问题,提出了一种新的目标自动鉴别方法,首先对CFAR检测的结果做基于面积特征的预鉴别处理,而后对获得的ROI目标切片提取鉴别特征,并在特征分析的基础上设定特征判决阈值,实现序贯鉴别处理。利用X波段SAR图像数据检验了上述方法,给出了鉴别输出的ROI切片。