机载反辐射导弹和常规反舰导弹协同规划算法

机载反辐射导弹在攻击移动目标时容易受雷达关机影响而丢失目标,和常规主动雷达制导的反舰导弹协同攻击可以有效对抗目标雷达关机。为了达到协同作战目的,载机起飞前已知目标信息情况下,通过计划协同算法确定协同作战方案,方案包括载机和导弹各个航路点位置和到达该位置的时间点,然后按照在机场起飞前就制定好的方案实施具体作战行动。对计划协同算法进行了实例仿真,结果表明算法切实有效。     

基于粒子滤波改进的VTS微弱目标检测前跟踪算法

舰船交通服务系统是民用雷达的信息集成系统,探测微弱目标存在RCS小、回波弱、杂波强等问题,导致信噪比低,难以实现有效检测跟踪。基于粒子滤波的检测前跟踪技术对低信噪比下微弱目标信息积累和探测有良好效果。通过采集单设备实测数据,构建遗忘因子和收敛因子以增加重采样的效率,引入虚拟采样保持粒子的多样性,提升粒子滤波对微弱目标的探测能力。仿真试验表明,改进后的算法可实现舰船交通服务系统对微弱目标的有效探测,并能获得较精准的目标状态估计值。     

Expansion planning for waste‐to‐energy systems using waste forecast prediction sets

We consider an expansion planning problem for Waste‐to‐Energy (WtE) systems facing uncertainty in future waste supplies. The WtE expansion plans are regarded as strategic, long term decisions, while the waste distribution and treatment are medium to short term operational decisions which can adapt to the actual waste collected. We propose a prediction set uncertainty model which integrates a set of waste generation forecasts and is constructed based on user‐specified levels of forecasting errors. Next, we use the prediction sets for WtE expansion scenario analysis. More specifically, for a given WtE expansion plan, the guaranteed net present value (NPV) is evaluated by computing an extreme value forecast trajectory of future waste generation from the prediction set that minimizes the maximum NPV of the WtE project. This problem is essentially a multiple stage min‐max dynamic optimization problem. By exploiting the structure of the WtE problem, we show this is equivalent to a simpler min‐max optimization problem, which can be further transformed into a single mixed‐integer linear program. Furthermore, we extend the model to optimize the guaranteed NPV by searching over the set of all feasible expansion scenarios, and show that this can be solved by an exact cutting plane approach. We also propose a heuristic based on a constant proportion distribution rule for the WtE expansion optimization model, which reduces the problem into a moderate size mixed‐integer program. Finally, our computational studies demonstrate that our proposed expansion model solutions are very stable and competitive in performance compared to scenario tree approaches. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 47–70, 2016     

Timing of information acquisition in a competitive environment

This article investigates the impact of timing on sellers' information acquisition strategies in a duopoly setting. Market uncertainty is captured by a representative consumer who has a private taste for the product's horizontal attribute, and both sellers can acquire this information either before (ex‐ante acquisition) or after (ex‐post acquisition) observing their own product qualities. We identify several conflicting effects of information acquisition that vary significantly in its timing and market characteristics. In the monopoly scenario, information acquisition is unambiguously beneficial and ex‐ante acquisition is the dominant option, because it helps a seller not only design the proper product but also craft better pricing strategy. By contrast, when there is competition, information acquisition eliminates the buffer role of market uncertainty and leads to the fiercest production or pricing competition, which makes the subsequent effects of acquisition detrimental, and a seller's payoff is nonmonotonic in terms of its acquisition cost. Moreover, compared with the ex‐ante information acquisition, ex‐post information acquisition normally generates higher sellers' equilibrium payoffs by postponing the timing of acquisition and maintaining product differentiation. Nonetheless, ex‐post information acquisition also provides the seller with greater acquisition incentive and occasionally makes him worse off than that in the ex‐ante scenario. Thus, in a competitive environment, having the option of information acquisition and flexibility in its timing can be both detrimental and irresistible. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 3–22, 2016     

分布式火控网的数据融合研究

针对火控网的特点,采用了分布式结构。分析了自适应滤波算法的原理,采用航迹融合算法对各探测器的跟踪航迹进行了融合,仿真分析融合前后的距离、方位、俯仰的误差大小。仿真结果表明,分布式火控网经过航迹融合后,系统的精度有较大改善,跟踪性能比融合前有大幅度提高。     

多源多目标信息融合仿真平台的分析与实现

多源多目标信息融合是当前军事科技的前沿,在武器控制数据链中有重要应用。开发对应的仿真平台,不但能减少实际设备开发带来的高投入,而且能对系统的性能做出深入全面的把握。仿真平台包括了5个工具,它们构成完整的从配置到性能评估的仿真测试流程。仿真工具是开放的,在教学和科研中,可以通过修改关键算法探讨提高系统性能的方法。     

对组网雷达的多目标航迹欺骗

由于目前点迹压缩、点迹串行合并等融合方法在组网雷达中的普遍应用,使得对单部雷达的多目标航迹欺骗很容易被雷达组网系统所识别。为了能够有效地欺骗组网雷达,并在网内各雷达的PPI显示器上显示相似的航迹,在对单部雷达实施多目标航迹欺骗的理论基础上,提出了一种对组网雷达实施多目标航迹欺骗的方法。根据组网雷达系统中各雷达坐标位置的不同以及各雷达主波束对准干扰机时间的不同,详细讨论了产生具有空间相关性和时间相关性的可控欺骗航迹的算法。     

Risk‐sensitive sizing of responsive facilities

We develop a risk‐sensitive strategic facility sizing model that makes use of readily obtainable data and addresses both capacity and responsiveness considerations. We focus on facilities whose original size cannot be adjusted over time and limits the total production equipment they can hold, which is added sequentially during a finite planning horizon. The model is parsimonious by design for compatibility with the nature of available data during early planning stages. We model demand via a univariate random variable with arbitrary forecast profiles for equipment expansion, and assume the supporting equipment additions are continuous and decided ex‐post. Under constant absolute risk aversion, operating profits are the closed‐form solution to a nontrivial linear program, thus characterizing the sizing decision via a single first‐order condition. This solution has several desired features, including the optimal facility size being eventually decreasing in forecast uncertainty and decreasing in risk aversion, as well as being generally robust to demand forecast uncertainty and cost errors. We provide structural results and show that ignoring risk considerations can lead to poor facility sizing decisions that deteriorate with increased forecast uncertainty. Existing models ignore risk considerations and assume the facility size can be adjusted over time, effectively shortening the planning horizon. Our main contribution is in addressing the problem that arises when that assumption is relaxed and, as a result, risk sensitivity and the challenges introduced by longer planning horizons and higher uncertainty must be considered. Finally, we derive accurate spreadsheet‐implementable approximations to the optimal solution, which make this model a practical capacity planning tool.© 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

Strategic capacity decision‐making in a stochastic manufacturing environment using real‐time approximate dynamic programming

In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

Route optimization for multiple searchers

We consider a discrete time‐and‐space route‐optimization problem across a finite time horizon in which multiple searchers seek to detect one or more probabilistically moving targets. This article formulates a novel convex mixed‐integer nonlinear program for this problem that generalizes earlier models to situations with multiple targets, searcher deconfliction, and target‐ and location‐dependent search effectiveness. We present two solution approaches, one based on the cutting‐plane method and the other on linearization. These approaches result in the first practical exact algorithms for solving this important problem, which arises broadly in military, rescue, law enforcement, and border patrol operations. The cutting‐plane approach solves many realistically sized problem instances in a few minutes, while existing branch‐and‐bound algorithms fail. A specialized cut improves solution time by 50[percnt] in difficult problem instances. The approach based on linearization, which is applicable in important special cases, may further reduce solution time with one or two orders of magnitude. The solution time for the cutting‐plane approach tends to remain constant as the number of searchers grows. In part, then, we overcome the difficulty that earlier solution methods have with many searchers. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010