基于候选轨迹统计模型的红外弱小点目标检测(英文)

建立候选目标轨迹集合,以候选目标轨迹集合为样本空间,利用沿候选轨迹的能量积分构造此空间上的概率分布,从而建立候选轨迹的概率统计模型.以此模型为基础,设计了一种弱小点目标检测方法.通过蒙特卡罗实验方法调节模型参数,使得算法的检测性能达到所提出的指标.该算法规避了以往算法中需要获得解析形式的候选轨迹密度函数的限制,因此能检测出沿任意方向作直线运动的点目标.最后,针对给定的弱小点目标观测图像序列,利用所提出的算法进行检测,仿真结果证明此算法是有效的.     

蒙特卡罗方法在研究J／ψ大横动量现象中的应用

利用美国相对论重离子对撞机（Relativistic Heavy Ion Collider，RHIC）提供的质子-质子碰撞中J／ψ产生随快度变化的最新实验数据，抽取了快度在作为随机变量时的概率分布密度函数，在建立的Glauber蒙特卡罗模型中研究了氘-金碰撞中J／ψ粒子产生的大横动量现象。经计算发现：在考虑到核遮蔽效应和核吸收效应后，理论结果能很好地解释最新实验现象。     

Performance and properties of Q‐Statistic monitoring schemes

In this article, we study the Shewhart chart of Q statistics proposed for the detection of process mean shifts in start‐up processes and short runs. Exact expressions for the run‐length distribution of this chart are derived and evaluated using an efficient computational procedure. The procedure can be considerably faster than using direct simulation. We extend our work to analyze the practice of requiring multiple signals from the chart before responding, a practice sometimes followed with Shewhart charts. The results show that waiting to receive multiple signals severely reduces the probability of quickly detecting shifts in certain cases, and therefore may be considered a risky practice. Operational guidelines for practitioners implementing the chart are discussed. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

What you should know about approximate dynamic programming

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well‐known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

基于QIPF的弱目标检测前跟踪算法

针对低信噪比条件下弱目标检测跟踪问题,提出一种拟蒙特卡罗智能粒子滤波检测前跟踪算法(Quasi-Monte Carlo Intelligent Particle Filter Track Before Detect,QIPF-TBD)。首先,该算法采用拟蒙特卡罗技术改善探测空间中粒子分布的均匀性;其次,通过对更新阶段的粒子进行交叉变异等操作,提高粒子重采样之后的多样性。与同类算法的仿真分析表明,所提方法能有效改善低信噪比目标的检测概率和跟踪精度。     

炮兵风险距离的仿真计算方法

现代战场非线性趋势愈加明显,炮兵火力对敌我双方以及平民都可能造成毁伤。合理确定炮兵风险距离,控制对非敌人员的杀伤风险是炮兵火力运用的重要内容。提出了炮兵风险距离的概念,并给出了仿真计算方法。首先,给出了炮兵风险距离的概念,并解释了其仿真计算原理。然后,建立了蒙特卡洛仿真模型。最后,结合实例验证了仿真计算方法的正确性。     

Carlo Bianco and Guerra per bande: an Italian approach to irregular warfare

Since the end of the Cold War, a huge debate over how war has been changing has emerged; a common feature is that modern conflicts are not state vs. state wars, but ‘irregular wars’. In order to better understand modern irregular warfare, it is important to analyse past authors and ideas. Carlo Bianco’s concept of Guerra per bande highlights elements of mobility with different cooperating units, of terror, and of complex terrain. The present study offers the first English analysis of Carlo Bianco and underlines the similarities of his work to the hybrid warfare concept.     

Consecutive k‐within‐m‐out‐of‐n:F system with exchangeable components

As a generalization of k‐out‐of‐n:F and consecutive k‐out‐of‐n:F systems, the consecutive k‐within‐m‐out‐of‐n:F system consists of n linearly ordered components such that the system fails iff there are m consecutive components which include among them at least k failed components. In this article, the reliability properties of consecutive k‐within‐m‐out‐of‐n:F systems with exchangeable components are studied. The bounds and approximations for the survival function are provided. A Monte Carlo estimator of system signature is obtained and used to approximate survival function. The results are illustrated and numerics are provided for an exchangeable multivariate Pareto distribution. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009