机动目标运动数学模型评述

近十几年来,人们对机动目标的跟踪问题进行了大量研究。问题的核心是如何建立机动目标运动的数学模型。本文主要对现有的空中机动目标的数学模型进行评述和比较研究。     

基于假设检验的编队组织决策设计

从集中式和分散式组织结构分析出发,采用假设检验的方法,探讨了分布式结构假设检验在海军舰艇编队环境评估中的组织决策设计,并从决策设计和算法上对其进行了复杂性分析.研究结果表明,基于假设检验的组织决策是一个NP问题,就存在的问题提出算法和组织设计研究的思路.     

多帧高斯混合概率假设密度的多目标跟踪算法

针对低检测概率下多目标跟踪时,概率假设密度滤波器难以正确估计当前目标个数以及目标状态问题,提出一种基于多帧融合的高斯混合概率假设密度滤波算法。根据不同时刻目标权值构造目标多帧权值记录集及目标状态抽取标志。当某些时刻目标被漏检时,依据目标状态抽取标志,并结合目标多帧权值记录集中权值信息估计丢失目标的状态。仿真实验表明,算法有效地提高了低检测概率下现有相关算法的目标状态和数目估计精度。     

DOES MILITARY EXPENDITURE MATTER FOR INFLATION AND ECONOMIC GROWTH?

This paper sets up a monetary endogenous growth model, and uses it to explain the ambiguous linkage between the military burden and the inflation rate observed in existing empirical studies. It is found that an expansion in the military burden has an ambiguous effect on the inflation rate depending upon the relative extent of two conflicting forces. More specifically, if the increase in the marginal benefit from holding money exceeds (falls short of) the increase in the marginal product of private capital, the inflation rate will rise (fall) in response. Moreover, it is found that an increase in the military burden will stimulate the balanced growth rate, confirming Benoit’s famous empirical findings.     

目标识别中的传感器管理方法

基于假设检验的方法,研究了目标识别中的传感器管理方法。分别以用于假设检验问题中的最大后验概率、最小代价函数和最大正确检测概率为目标函数,给出了用于目标识别问题的多传感器管理方法,并针对目标识别问题的特点,对其进行了相应的改进。通过对一定场景的仿真,比较了三种不同方法的识别正确率和平均采样次数,分析了各种方法的特点。     

竞争性假设分析法在群体性事件情报预警中的应用

目前,边防部门对于群体性事件预警情报的分析缺乏科学系统的工作流程和分析思路,受思维定势、先入为主等主观因素的影响较为严重。随着科学技术的不断发展,群体性事件预警情报的来源不断扩展,数量不断攀升,如何进行准确的情报分析,成为边防部门面临的一个亟待解决的问题。分析了竞争性假设分析法在群体性事件情报预警中的可行性和实际应用,对预警情报的分析工作有一定的借鉴价值。     

实时仿真计算机系统的若干问题

本文比较了实时仿真计算机系统中配备A/D、D/A变换器的两种方案。分析了几种检索算法,并且提出了一种新的全并行检索方法——映象检索。文中论述了仿真计算任务的特点及其并行性的开发。本文在分析了AD-10的缺点之后指出,纯异构型仿真专用多处理机的局限性较大,不能充分开发仿真计算任务的并行性。较好的结构形式应是以同构型多处理机为核心。     

机动目标建模及机动检测算法

为解决机动目标跟踪问题,建立了非机动(匀速直线运动)和机动目标当前统计两种动态模型,并对机动目标当前统计模型的输入控制的估计进行了适当改进.同时对非机动模型的观测残差和机动模型的输入估计进行检验,以便准确检测目标机动.     

Pairwise model discrimination with applications in lifetime distributions and degradation processes

Reliability data obtained from life tests and degradation tests have been extensively used for purposes such as estimating product reliability and predicting warranty costs. When there is more than one candidate model, an important task is to discriminate between the models. In the literature, the model discrimination was often treated as a hypothesis test and a pairwise model discrimination procedure was carried out. Because the null distribution of the test statistic is unavailable in most cases, the large sample approximation and the bootstrap were frequently used to find the acceptance region of the test. Although these two methods are asymptotically accurate, their performance in terms of size and power is not satisfactory in small sample size. To enhance the small‐sample performance, we propose a new method to approximate the null distribution, which builds on the idea of generalized pivots. Conventionally, the generalized pivots were often used for interval estimation of a certain parameter or function of parameters in presence of nuisance parameters. In this study, we further extend the idea of generalized pivots to find the acceptance region of the model discrimination test. Through extensive simulations, we show that the proposed method performs better than the existing methods in discriminating between two lifetime distributions or two degradation models over a wide range of sample sizes. Two real examples are used to illustrate the proposed methods.     

基于交互式多模型的多假设航迹起始算法

多假设(MHT)方法是一种理想的航迹起始算法,将交互式多模型算法(IMM)应用于MHT算法中(IMMMHT),改进了MHT算法在滤波时单独采用一个模型会受到模型自身局限性的影响使得滤波的精度不高的缺点.通过仿真表明,IMMMHT算法在虚警概率、快速性和跟踪精度上都优于原MHT算法.