C4KISR系统成熟度的评价方法及其成熟度模型

针对指挥自动化系统与武器系统一体化(即C4KISR)的演进过程及发展趋势,探索C4KISR系统成熟度的有关问题。在对C4KISR系统的内涵、组成、功能和主要特征作简要分析之后,提出了评价系统成熟度的两种方法,构建了系统成熟度模型框架,并针对其中一种方法建立了系统成熟度模型。该模型为衡量C4KISR系统的发展水平提供了方法。     

面向卫星的在轨服务任务规划方法

针对空间在轨服务日趋成熟以及在轨服务现实需求增长的背景,以在轨服务航天器为研究对象,研究面向卫星的在轨服务任务规划问题,探讨如何合理安排与调配在轨服务资源。将问题分解为在轨服务资源分配和在轨服务路径规划两层,并建立双层优化数学模型。设计在轨服务任务规划算法求解问题,包括基于多种群并行进化的混沌遗传算法和基于全局坐标转换的NSGA-Ⅱ+GSDE算法,并通过仿真结果对比分析,验证算法的可行性和有效性。     

一种传感器网络时空融合模型

针对多传感器间存在时空相关性和先验知识未知的情况,提出了一种基于核偏最小二乘最优加权的传感器网络时空融合模型。首先用核偏最小二乘法对每个传感器在不同时刻的测量值进行融合,然后将各个传感器同时刻的估计值进行空间最优融合,从而得到被测参量的最终融合估计。该模型还能排除那些取值远离样本点集合平均水平的奇异点。理论分析和融合实验表明,该模型能取得较高的融合精度,并能显著节省网络能量。     

预定增益理论在导弹飞行控制系统中的应用

针对导弹在飞行过程中动力学特性大范围变化的特点,以俯仰通道为例,运用预定增益控制理论设计导弹飞行控制系统,即利用导弹空气动力学特性和导弹某些特征参数的强相关性来调整控制器参数,确保导弹在要求的飞行条件下满足稳定性和动态品质.最后通过仿真验证了所设计的控制器满足性能要求.     

基于随机搜索策略的中继卫星调度方法

高质量的调度方案不仅能满足用户的需求,还能为中继卫星系统的计划编制提供科学的决策手段和依据。针对日益多样化的用户需求,采用全新的中继卫星调度应用模式,允许用户提交多个可以滑动的时间窗口。面向这种调度模式,考虑中继业务中任务调度的灵活性和任务间的冲突,构建考虑多滑动窗口的中继卫星调度模型,并设计基于随机搜索策略的中继卫星调度算法。算法包括任务资源匹配与邻域生成、可用时间段生成、任务冲突分析、邻域搜索与冲突消解以及资源与任务集更新5个算子。通过仿真实验将该算法与基于时间自由度的启发式算法进行对比,验证了算法的有效性。     

An exact branch‐and‐price algorithm for multitasking scheduling on unrelated parallel machines

We consider the multitasking scheduling problem on unrelated parallel machines to minimize the total weighted completion time. In this problem, each machine processes a set of jobs, while the processing of a selected job on a machine may be interrupted by other available jobs scheduled on the same machine but unfinished. To solve this problem, we propose an exact branch‐and‐price algorithm, where the master problem at each search node is solved by a novel column generation scheme, called in‐out column generation, to maintain the stability of the dual variables. We use a greedy heuristic to obtain a set of initial columns to start the in‐out column generation, and a hybrid strategy combining a genetic algorithm and an exact dynamic programming algorithm to solve the pricing subproblems approximately and exactly, respectively. Using randomly generated data, we conduct numerical studies to evaluate the performance of the proposed solution approach. We also examine the effects of multitasking on the scheduling outcomes, with which the decision maker can justify making investments to adopt or avoid multitasking.     

A non-myopic scheduling method of radar sensors for maneuvering target tracking and radiation control

In decades, the battlefield environment is becoming more and more complex with plenty of electronic equipments. Thus, in order to improve the survivability of radar sensors and satisfy the requirement of maneuvering target tracking with a low probability of intercept, a non-myopic scheduling is proposed to minimize the radiation cost with tracking accuracy constraint. At first, the scheduling problem is formulated as a partially observable Markov decision process (POMDP). Then the tracking accuracy and radiation cost over the future finite time horizon are predicted by the posterior carmér-rao lower bound (PCRLB) and the hidden Markov model filter, respectively. Finally, the proposed scheduling is implemented efficiently by utilizing the branch and bound (B&B) pruning algorithm. Simulation results show that the performance of maneuvering target tracking was improved by the improved interacting multiple model (IMM), and the scheduler time and maximum memory consumption were significant reduced by the present B&B pruning algorithm without losing the optimal solution.     

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.     

Two‐machine flow shop scheduling with common due window to minimize weighted number of early and tardy jobs

This article studies two due window scheduling problems to minimize the weighted number of early and tardy jobs in a two‐machine flow shop, where the window size is externally determined. These new scheduling models have many practical applications in real life. However, results on these problems have rarely appeared in the literature because of a lack of structural and optimality properties for solving them. In this article, we derive several dominance properties and theorems, including elimination rules and sequencing rules based on Johnsos order, lower bounds on the penalty, and upper bounds on the window location, which help to significantly trim the search space for the problems. We further show that the problems are NP‐hard in the ordinary sense only. We finally develop efficient pseudopolynomial dynamic programming algorithms for solving the problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

某型飞机大气数据系统设计

大气数据系统是重要的机载电子设备,其性能、精度直接关系到飞机的飞行安全.在介绍某型号飞机大气数据系统的软硬件体系结构的基础上,从三个方面深入研究了提高大气数据系统精度的途径:大气数据计算模型;基于BP神经网络的压力测量温度误差补偿方法;一种计算型静压源误差补偿方法.其大气数据系统系统性能稳定,结构紧凑,速度快,精度高,软硬件升级简单.试验证明其大气数据系统能很好满足某型号飞机的使用要求.