Optimal source placement for point coverage in active multistatic sonar networks

The allocation of underwater sensors for tracking, localization, and surveillance purposes is a fundamental problem in anti-submarine warfare. Inexpensive passive receivers have been heavily utilized in recent years; however, modern submarines are increasingly quiet and difficult to detect with receivers alone. Recently, the idea of deploying noncollocated sources and receivers has emerged as a promising alternative to purely passive sensor fields and to traditional sonar fields composed of collocated sources and receivers. Such a multistatic sonar network carries a number of advantages, but it also brings increased system complexity resulting from its unusual coverage patterns. In this work, we study the problem of optimally positioning active multistatic sonar sources for a point coverage application where all receivers and points of interest are fixed and stationary. Using a definite range sensor model, we formulate exact methods and approximation algorithms for this problem and compare these algorithms via computational experiments. We also examine the performance of these algorithms on a discrete approximation of a continuous area coverage problem and find that they offer a significant improvement over two types of random sensor deployment.     

A robust sensor covering and communication problem

We consider the problem of placing sensors across some area of interest. The sensors must be placed so that they cover a fixed set of targets in the region, and should be deployed in a manner that allows sensors to communicate with one another. In particular, there exists a measure of communication effectiveness for each sensor pair, which is determined by a concave function of distance between the sensors. Complicating the sensor location problem are uncertainties related to sensor placement, for example, as caused by drifting due to air or water currents to which the sensors may be subjected. Our problem thus seeks to maximize a metric regarding intrasensor communication effectiveness, subject to the condition that all targets must be covered by some sensor, where sensor drift occurs according to a robust (worst‐case) mechanism. We formulate an approximation approach and develop a cutting‐plane algorithm to solve this problem, comparing the effectiveness of two different classes of inequalities. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 582–594, 2015     

斜拉桥监测系统传感器位置的寻优

分析了传感器系统尤其是传感器布点位置在桥梁结构健康监测中的重要性,对桥梁结构传感器优化布点作了详细的分类及论述。分析了遗传算法的特点和优点,并将其与传统优化算法作了比较。利用遗传算法研究了宁波招宝山独塔不对称斜拉桥健康监测系统中传感器的最优布点问题。结果表明,用遗传算法进行传感器最优布点具有结果稳定可靠,收敛迅速的特点,能够满足系统的要求。     

Optimal sensor placement for underwater threat detection

The optimization framework for optimal sensor placement for underwater threat detection has been developed. It considers single‐period and multiperiod detection models, each of which includes two components: detection algorithm and optimization problem for sensor placement. The detection algorithms for single‐period and multiperiod models are based on likelihood ratio and sequential testing, respectively. For the both models, the optimization problems use the principle of superadditive coverage, which is closely related to energy‐based and information‐based approaches. An algorithm for quasi‐regular sensor placement approximating solutions to the optimization problems has been developed based on corresponding continuous relaxations and a criterion for its applicability has been obtained. Numerical experiments have demonstrated that the algorithm consistently outperforms existing optimization techniques for optimal sensor placement.© 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

基于Rényi信息增量的机动目标协同跟踪算法

针对基于多传感器组网进行机动目标跟踪的传感器管理问题,提出了一种基于Rényi信息增量的机动目标协同跟踪算法。首先结合"当前"统计模型和交互式多模型不敏卡尔曼滤波算法设计了一种变结构多模型算法,来进行机动目标的状态估计;然后以Rényi信息增量为评价准则,选择使Rényi信息增量最大的单个传感器进行目标跟踪;最后利用得到的最优加速度估计进行网格划分,更新变结构多模型中的模型集合。在一般机动及强机动场景下进行了算法性能分析,仿真结果表明,该算法能够合理地选择传感器,提高了对机动目标的跟踪精度。     

固定节点3D网格部署的水下传感器网络分簇路由算法

水下传感器网络是应用于水下通信的重要传感器网络技术。提出了基于固定节点3D网格部署的水下无线传感器网络分簇算法,设计了3D网格的编址和分簇方法,实现了基于地址分配的节点定位,构建了算法的能耗分析模型。采用MATLAB完成了算法的性能仿真,对比了DS-VBF、IAR和GEDAR 3个算法的平均数据传输延迟和网络生存时间(TTL)。实验结果表明,此算法的平均数据传输延迟较短,可明显提高UWSNS的生存时间。     

Assortment optimization with position effects under the nested logit model

We study the assortment optimization problem with position effects under the nested logit model, whose goal is to find the revenue-maximizing subset of products as well as their corresponding display positions. In this joint assortment-position optimization problem, the choices of products are affected by not only their qualities and prices but also the positions where they are displayed. Despite determining the assortment and their corresponding display positions sequentially, we propose to solve this problem in an integrated way to obtain the optimal solution. We formulate this problem as a nonlinear binary integer programming model and develop a dynamic programming based solution approach to obtain the optimal assortment-position assignments. We carry out extensive numerical experiments to evaluate the benefit of our integrated approach. The most important insight we discover is that it is not necessarily better to put the most attractive products in the best position. Moreover, we show that compared to the sequential approaches, our approach can improve revenue by 10.38% on average, which suggests that firms should take into consideration position effects when making assortment decisions. Finally, we discuss results related to two extensions of this problem, that is, the special case when positions are preassigned to nests, and the joint assortment-position-price optimization problem.     

Optimal maintenance policies for a safety‐critical system and its deteriorating sensor

We consider the integrated problem of optimally maintaining an imperfect, deteriorating sensor and the safety‐critical system it monitors. The sensor's costless observations of the binary state of the system become less informative over time. A costly full inspection may be conducted to perfectly discern the state of the system, after which the system is replaced if it is in the out‐of‐control state. In addition, a full inspection provides the opportunity to replace the sensor. We formulate the problem of adaptively scheduling full inspections and sensor replacements using a partially observable Markov decision process (POMDP) model. The objective is to minimize the total expected discounted costs associated with system operation, full inspection, system replacement, and sensor replacement. We show that the optimal policy has a threshold structure and demonstrate the value of coordinating system and sensor maintenance via numerical examples. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 399–417, 2017     

基于跟踪精度控制的多传感器管理方法

针对地面机动目标跟踪过程中的多传感器管理问题展开了研究，设计了一种基于跟踪精度控制的多传感器多目标分配方法。首先，在考虑目标与目标之间、目标与传感器之间和传感器与传感器之间等的多种约束条件下运用基于协方差控制的思想建立了多传感器多目标分配问题的优化模型；接着将等价伪量测的异步融合算法与IMM算法结合，计算各目标在不同融合周期的跟踪精度估计值；最后，以目标的跟踪精度需求为出发点，结合蚁群算法的思想，设计了一种求解所建立的多传感器多目标分配问题的优化模型的算法。仿真结果表明：该管理方法能在确保跟踪精度需求的前提下，根据对各目标跟踪任务的重要程度，合理地调度传感器资源。     

An adaptive dynamic programming algorithm for a stochastic multiproduct batch dispatch problem

We address the problem of dispatching a vehicle with different product classes. There is a common dispatch cost, but holding costs that vary by product class. The problem exhibits multidimensional state, outcome and action spaces, and as a result is computationally intractable using either discrete dynamic programming methods, or even as a deterministic integer program. We prove a key structural property for the decision function, and exploit this property in the development of continuous value function approximations that form the basis of an approximate dispatch rule. Comparisons on single product‐class problems, where optimal solutions are available, demonstrate solutions that are within a few percent of optimal. The algorithm is then applied to a problem with 100 product classes, and comparisons against a carefully tuned myopic heuristic demonstrate significant improvements. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 742–769, 2003.     

多传感器多目标定位与跟踪技术研究

针对炮兵打击目标的特性和获取目标信息所采用的侦察设施 ,研究了多传感器多目标定位和跟踪问题。在闭圆和聚类概念的基础上 ,提出了一种多传感器多目标融合算法 ,并给出了状态估计的最优解。仿真结果证明了这种算法的有效性。这一模型的物理实现正在进一步研究之中。     

Resource allocation with lumpy demand: To speed or not to speed?

In the classical EPQ model with continuous and constant demand, holding and setup costs are minimized when the production rate is no larger than the demand rate. However, the situation may change when demand is lumpy. We consider a firm that produces multiple products, each having a unique lumpy demand pattern. The decision involves determining both the lot size for each product and the allocation of resources for production rate improvements among the products. We find that each product's optimal production policy will take on only one of two forms: either continuous production or lot‐for‐lot production. The problem is then formulated as a nonlinear nonsmooth knapsack problem among products determined to be candidates for resource allocation. A heuristic procedure is developed to determine allocation amounts. The procedure decomposes the problem into a mixed integer program and a nonlinear convex resource allocation problem. Numerical tests suggest that the heuristic performs very well on average compared to the optimal solution. Both the model and the heuristic procedure can be extended to allow the company to simultaneously alter both the production rates and the incoming demand lot sizes through quantity discounts. Extensions can also be made to address the case where a single investment increases the production rate of multiple products. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Solving partially observable agent‐intruder games with an application to border security problems

In this paper, we introduce partially observable agent‐intruder games (POAIGs). These games model dynamic search games on graphs between security forces (an agent) and an intruder given possible (border) entry points and high value assets that require protection. The agent faces situations with dynamically changing, partially observable information about the state of the intruder and vice versa. The agent may place sensors at selected locations, while the intruder may recruit partners to observe the agent's movement. We formulate the problem as a two‐person zero‐sum game, and develop efficient algorithms to compute each player's optimal strategy. The solution to the game will help the agent choose sensor locations and design patrol routes that can handle imperfect information. First, we prove the existence of ?‐optimal strategies for POAIGs with an infinite time horizon. Second, we introduce a Bayesian approximation algorithm to identify these ?‐optimal strategies using belief functions that incorporate the imperfect information that becomes available during the game. For the solutions of large POAIGs with a finite time horizon, we use a solution method common to extensive form games, namely, the sequence form representation. To illustrate the POAIGs, we present several examples and numerical results.     

A multi‐stage stochastic programming approach for network capacity expansion with multiple sources of capacity

In networks, there are often more than one sources of capacity. The capacities can be permanently or temporarily owned by the decision maker. Depending on the nature of sources, we identify the permanent capacity, spot market capacity, and contract capacity. We use a scenario tree to model the uncertainty, and build a multi‐stage stochastic integer program that can incorporate multiple sources and multiple types of capacities in a general network. We propose two solution methodologies for the problem. Firstly, we design an asymptotically convergent approximation algorithm. Secondly, we design a cutting plane algorithm based on Benders decomposition to find tight bounds for the problem. The numerical experiments show superb performance of the proposed algorithms compared with commercial software. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 600–614, 2017     

基于相对观测量的多机器人定位

研究了多机器人队列利用相对观测信息在未知环境中进行同时定位的问题。当队列中某个机器人观测到另外一个或几个机器人时,利用这些信息来同时更新整个队列的位置及协方差矩阵,也即整个队列共享所获得的观测,来得到更精确的位置估计。每个机器人都携带内部及外部传感器,内部传感器感知机器人自身的运动,外部传感器能提供机器人之间的相对观测量,如相对距离和相对方位。利用扩展卡尔曼滤波(EKF)算法融合内部及外部传感器信息,对多机器人队列进行同时定位;并对不同的观测量及机器人个数进行了仿真分析,给出了不同情况下的滤波器结构,研究比较了它们的定位精度。仿真结果表明,利用机器人之间的相对观测信息,可以显著提高定位精度。     

船艇液压设备故障检测诊断测点优化分析

在介绍液压传动控制技术在船艇装备应用的基础上,分析了液压系统的特点及其故障定位的困难性,提出了测点优化选择的重要性和现实性。依据成熟的图论理论、数学方法及测点布置的相关研究,结合已有的测点布置技术,对测点布置在液压设备上的优化选择进行了分析。     

Optimal stopping in software testing

In this paper we address the problem of how to decide when to terminate the testing/modification process and to release the software during the development phase. We present a Bayesian decision theoretic approach by formulating the optimal release problem as a sequential decision problem. By using a non‐Gaussian Kalman filter type model, proposed by Chen and Singpurwalla (1994), to track software reliability, we are able to obtain tractable expressions for inference and determine a one‐stage look ahead stopping rule under reasonable conditions and a class of loss functions. © 2002 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

基于最优分离矩阵循环迭代的盲源分离次序调整算法

文章针对盲源分离中输出信号帧与帧之间信号衔接顺序的不确定性问题,提出了最优分离矩阵循环迭代算法,将每帧信号分离时目标函数寻优所得分离矩阵作为下一帧循环的初始化矩阵进行迭代来分离信号。理论分析与计算机仿真表明:该算法能有效解决盲分离中信号次序的不确定性问题,具有较强的实际应用价值。     

舰载红外警戒系统多目标跟踪算法研究

多目标跟踪有着十分广泛的应用,目前国内外多目标跟踪的研究重点都集中在雷达等主动传感器上,但是对于红外的多目标跟踪领域却研究较少。对于红外系统,目标跟踪的最大特点是观测量只有角度,没有距离,这就使得传统的跟踪滤波问题遇到困难。通过讨论舰载红外警戒系统的多目标跟踪算法,分析目标与传感器的特点,提出了一整套多目标跟踪的算法。仿真结果表明,此套算法能满足工程应用的要求。     

被动传感器融合检测系统的优化分布密度

被动传感器由于采用无源探测,而且制造成本低,体积小,在战场环境中将采用并配置大量的被动传感器来检测目标.不论是主动还是被动式传感器,在实际应用中均存在资源优化配置问题.针对被动传感器的具体应用模型,采取了若干合理近似,得到了在"OR"融合检测条件下,基于最大检测概率的被动传感器优化配置密度计算公式.