宽频带瞬态信号测试方法及应用

本文在分析高速数据采集电路的基础上,采用单片机技术,设计了一种通用性能好、分析频带宽的瞬态信号测试系统。该系统成功地解决了采样速度和频率分辩率的矛盾,提供了一种解决瞬态波形测量的有效方法。     

m 序列的完备递归采样法

本文提出了m 序列递归采样的概念,研究并解决了素数周期m 序列的完备递归采样,对周期为合数的情况得到了一些有用的结论。     

中口径舰炮对空闭环校射

首先建立了中口径舰炮武器系统对空闭环校射数学模型,比较新颖地解决了波门指示、弹序判别、滤波方法、脱靶量的求取和预测以及漏测弹丸的措施等一系列问题,通过仿真模拟明显地提高了系统精度和命中率,并使其工程化嵌入某型航炮系统,在工程上得以实现     

动态飞行求解双35标准值方法

通过顺解法,利用录取的目标现在点数据,已知的射表数据及对有关偏差量的修正,可求解出斜距离４５００ｍ,高低角０°至８４°范围内的标准值     

GNSS接收机离散化处理对解扩性能的影响

建立扩频信号防混叠滤波、采样、量化与解扩输出关系的数学解析模型,推导得到解扩输出信噪比的解析表达式。分析与仿真表明,当量化位数大于等于4 bit,解扩得到总的信噪比损失可以分解为由量化引起的损失和滤波加采样引起损失的乘积,且量化器最优限幅系数只与量化位数相关;当量化位数小于4 bit时,信噪比损失在一定条件下可近似为量化损失和滤波加采样损失的乘积。当量化位数大于4 bit、滤波器带宽大于5倍码率、采样频率大于4倍码率时,再增大上述参数引起的信噪比损失波动小于0.05 dB,对解扩性能提升不明显。该结论可为实用型全球导航卫星系统接收机前端离散化处理优化设计提供理论指导。     

Combined variance reduction techniques in fully sequential selection procedures

In the past several decades, many ranking‐and‐selection (R&S) procedures have been developed to select the best simulated system with the largest (or smallest) mean performance measure from a finite number of alternatives. A major issue to address in these R&S problems is to balance the trade‐off between the effectiveness (ie, making a correct selection with a high probability) and the efficiency (ie, using a small total number of observations). In this paper, we take a frequentist's point of view by setting a predetermined probability of correct selection while trying to reduce the total sample size, that is, to improve the efficiency but also maintain the effectiveness. In particular, in order to achieve this goal, we investigate combining various variance reduction techniques into the fully sequential framework, resulting in different R&S procedures with either finite‐time or asymptotic statistical validity. Extensive numerical experiments show great improvement in the efficiency of our proposed procedures as compared with several existing procedures.     

Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty

We propose a novel simulation‐based approach for solving two‐stage stochastic programs with recourse and endogenous (decision dependent) uncertainty. The proposed augmented nested sampling approach recasts the stochastic optimization problem as a simulation problem by treating the decision variables as random. The optimal decision is obtained via the mode of the augmented probability model. We illustrate our methodology on a newsvendor problem with stock‐dependent uncertain demand both in single and multi‐item (news‐stand) cases. We provide performance comparisons with Markov chain Monte Carlo and traditional Monte Carlo simulation‐based optimization schemes. Finally, we conclude with directions for future research.     

加权采样的序列蒙特卡罗滤波技术

在处理非线性滤波问题时,常用的几种通过模型近似或计算近似的滤波方法均在某种特定的情况下具有适用性.近年来随着计算机处理能力的快速发展,提出了序列蒙特卡罗滤波方法,因其在处理复杂的非线性和非高斯问题时表现出强大的潜力而引起广泛关注.详述了序列蒙特卡罗滤波算法的基本思想和原理,对其关键技术进行了归纳分析,并指出了该方法亟待解决的一些难点问题.     

无线传感器网络中多传感器动态自适应调度算法

无线传感器网络中,目标跟踪往往通过节点之间的协作完成。在无迹变换卡尔曼滤波基础上,提出一种多传感器动态自适应调度算法进行目标跟踪。该方法根据预测跟踪精度来确定采样间隔,然后基于特定的检测概率,为下一时刻选择一组传感器,形成一个临时工作组,并指定某一个传感器作为中心节点进行数据融合。仿真结果证明了该算法能有效提高跟踪精度和可靠性。     

Double window acceptance sampling

We introduce the concept of a sequential double window procedure and compare the resulting acceptance test with both a fixed sample size method as well as with the Wald sequential probability ratio test. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004