闪烁噪声条件下基于交互多模框架的雷达目标跟踪算法

针对传统雷达目标跟踪算法在处理闪烁噪声时面临的性能下降问题,提出一种将容积卡尔曼估计器与交互多模框架相结合的高性能滤波算法。该算法将目标状态建模为高斯分布,将闪烁噪声建模为混合高斯分布,同时将其发生概率建模为一阶马尔可夫过程;在此基础上,利用交互多模框架实现对不同高斯噪声分量的匹配滤波处理。为了减轻非线性观测条件对目标跟踪精度的影响,进一步采用容积卡尔曼估计器作为高斯近似滤波器,对目标状态进行递推预测和更新。仿真结果表明：所提算法较传统高斯混合滤波器和粒子滤波器具有更高的跟踪精度和更好的实时性能,同时还能对闪烁噪声出现时刻进行有效的估计。     

在非高斯信道下多用户检测的鲁棒算法

在无线通信中应用多用户检测技术可以有效去除多路径干扰 (MAI)。实验证明 ,实际信道通常包含冲激噪声 ,从而是非高斯的。讨论了目前扩频通信 (DS SS)中应用于非高斯信道的自适应鲁棒的多用户检测技术 ,以前的研究大多存在两个缺点 :一是需要对非高斯信道的模型有一个先验估计 ;二是计算较基于高斯信道的算法复杂的多。本文从传统的特征空间的自适应跟踪技术PASTd[1] ,针对其误差模型对算法进行修改 ,提出了一种简易的鲁棒算法。经蒙特卡洛仿真证明 ,该方法在含冲激噪声信道中能显著提高信号子空间跟踪的精度     

基于改进Markov模型软件测试策略

针对已有软件测试Markov模型与工程实践不符的情况，通过引入软件需求覆盖率改进Markov模型。在改进的Markov模型基础上，本文以软件测试过程中测试总代价最小为控制目标，采用交叉熵方法修正测试剖面，由优化测试剖面生成测试用例序列。仿真结果表明这种方法能够有效地降低软件测试总代价，是一种有效的软件测试方法。     

Exploiting self‐canceling demand point aggregation error for some planar rectilinear median location problems

When solving location problems in practice it is quite common to aggregate demand points into centroids. Solving a location problem with aggregated demand data is computationally easier, but the aggregation process introduces error. We develop theory and algorithms for certain types of centroid aggregations for rectilinear 1‐median problems. The objective is to construct an aggregation that minimizes the maximum aggregation error. We focus on row‐column aggregations, and make use of aggregation results for 1‐median problems on the line to do aggregation for 1‐median problems in the plane. The aggregations developed for the 1‐median problem are then used to construct approximate n‐median problems. We test the theory computationally on n‐median problems (n ≥ 1) using both randomly generated, as well as real, data. Every error measure we consider can be well approximated by some power function in the number of aggregate demand points. Each such function exhibits decreasing returns to scale. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 614–637, 2003.     

Comparative evaluation of prior versus progressive articulation of preference in bicriterion optimization

Procedures for solving multiple criteria problems are receiving increasing attention. Two major solution approaches are those involving prior articulation and progressive articulation of preference information. A progressive articulation (interactive) optimization approach, called the Paired Comparison Method (PCM) is compared to the prior articulation approach of a priori utility function measurement in a quality control decision environment from the perspective of the decision maker. The three major issues investigated included: (1) the ease of use of each method, (2) the preferences of solutions obtained, and (3) the insight provided by the methodology into the nature and structure of the problem. The problem setting involved management students who were rquired to determine an acceptance sampling plan using both methods. The PCM provided the most preferred solutions and was considered easier to use and understand. The prior articulation of preference method was found to give more insight into the problem structure. The results suggest that a hybrid approach, combining both prior preference assessment and an interactive procedure exploiting the advantages of each, should be employed to solve multiple criteria problems.     

A simple Markov model for the analysis of multiple air combat

It is proposed to describe multiple air-to-air combat having a moderate number of participants with the aid of a stochastic process based on end-game duels. A simple model describing the dominant features of air combat leads to a continuous time discrete-state Markov process. Solution of the forward Kolmogorov equations enables one to investigate the influence of initial force levels and performance parameters on the outcome probabilities of the multiple engagement. As is illustrated, such results may be useful in the decision-making process for aircraft and weapon system development planning. Some comparisons are made with Lanchester models as well as with a semi-Markov model.     

基于相位估计的湍流降质图像盲复原

地基望远镜观测的空间目标图像受大气湍流的影响,其分辨率受到很大的限制。为了提高湍流降质图像的复原效果,提出一种改进的盲解卷积方法。首先,考虑观测图像受到高斯噪声和泊松噪声的干扰,推导出基于混合噪声模型的盲解卷积代价函数;然后,根据傅里叶光学原理,利用波前相位表示点扩展函数,将点扩展函数从像素值估计转换为参数估计;最后,通过参数化表示方式,将代价函数寻优从约束最优化问题转换为无约束最优化问题。模拟实验结果验证了本文模型与数值算法的有效性。     

Case‐based reasoning and improved adaptive search for project scheduling

Most scheduling problems are notoriously intractable, so the majority of algorithms for them are heuristic in nature. Priority rule‐based methods still constitute the most important class of these heuristics. Of these, in turn, parametrized biased random sampling methods have attracted particular interest, due to the fact that they outperform all other priority rule‐based methods known. Yet, even the “best” such algorithms are unable to relate to the full range of instances of a problem: Usually there will exist instances on which other algorithms do better. We maintain that asking for the one best algorithm for a problem may be asking too much. The recently proposed concept of control schemes, which refers to algorithmic schemes allowing to steer parametrized algorithms, opens up ways to refine existing algorithms in this regard and improve their effectiveness considerably. We extend this approach by integrating heuristics and case‐based reasoning (CBR), an approach that has been successfully used in artificial intelligence applications. Using the resource‐constrained project scheduling problem as a vehicle, we describe how to devise such a CBR system, systematically analyzing the effect of several criteria on algorithmic performance. Extensive computational results validate the efficacy of our approach and reveal a performance similar or close to state‐of‐the‐art heuristics. In addition, the analysis undertaken provides new insight into the behaviour of a wide class of scheduling heuristics. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 201–222, 2000     

二维衰减谐波估计的四阶混合累积量方法

针对二维衰减谐波估计方法在大样本容量条件下计算量过大的问题,提出了一种基于二维四阶混合累积量的ESPRIT方法。在不影响估计精度的前提下,通过保持特征矩阵结构不变,有效地减小了算法的计算量。利用四阶混合累积量对高斯噪声的自动抑制,降低了参数估计的信噪比门限并提高了估计精度。仿真实验表明,在高斯色噪声条件下该方法的计算效率和估计性能优于现有方法。     

Dichotomous categorical variable formation in mathematical programming discriminant analysis models

Classification models, whether generated by statistical techniques or mathematical programming (MP) discriminant analysis methods, are often simplified by ad hoc formation of dichotomous categorical variables from the original variables with, for example, a dichotomous variable taking value 1 if the original variable is above a threshold level and 0 otherwise. In this paper an MP discriminant analysis method is developed for forming dichotomous categorical variables in problems with discriminant functions that are monotone in the original variables. For each of the original variables from which dichotomous variables may be formed, a set of possible threshold levels for dichotomous variable formation is defined. An MP model is then used to determine both the threshold level for forming each dichotomous variable and the associated discriminant function coefficient. The proposed MP approach is applied to a published problem and a number of simulated problem sets. It is shown that the discriminant functions in dichotomous categorical variables generated by this new MP approach can in some cases outperform the functions generated by standard MP discriminant analysis models using the original variables. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

A method for estimating the parameters of reliability models

The maximum likelihood estimates (MLE) of transition rates in a finite-state continuous-time Markov process with an absorbing state are obtained, when there are a number of independent realizations of the process. This process is explained as a model for deteriorating systems of reliability theory through example. The asymptotic properties of MLES as the observed number of realizations tend to infinity are studied. Extension of these results to semi-Markov processes is discussed.     

The single server queue in discrete time-numerical analysis I

This is the first of a sequence of papers dealing with the computational aspects of the transient behavior of queues in discrete time It is shown that for a substantial class of queues of practical interest, a wealth of numerical information may be obtained by relatively unsophisticated methods This approach should prove useful in the analysis of unstable queues which operate over a limited time interval, but is by no means limited to such queues Mathematically the service unit is modeled in terms of a multivariate Markov chain, whose particular structure is used in iterative computation. Many important queue features may then be derived from the n-step transition probabilities of this chain.     

Limiting behavior of the stochastic sequential assignment problem

The stochastic sequential assignment problem (SSAP) considers how to allocate available distinct workers to sequentially arriving tasks with stochastic parameters such that the expected total reward obtained from the sequential assignments is maximized. Implementing the optimal assignment policy for the SSAP involves calculating a new set of breakpoints upon the arrival of each task (i.e., for every time period), which is impractical for large‐scale problems. This article studies two problems that are concerned with obtaining stationary policies, which achieve the optimal expected reward per task as the number of tasks approaches infinity. The first problem considers independent and identically distributed (IID) tasks with a known distribution function, whereas in the second problem tasks are derived from r different unobservable distributions governed by an ergodic Markov chain. The convergence rate of the expected reward per task to the optimal value is also obtained for both problems. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

水雾扩散模型仿真

建立水雾红外衰减模型时,通常把水雾空间看作单一均匀整体处理,然而雾滴的扩散现象使水雾的雾滴密度无法保证处处相等,所以仿真结果与实际情况相比普遍存在较大偏差。针对这一问题,基于高斯扩散理论与Mie散射理论,提出了一种新的建模方案。通过将水雾空间划分为多个子空间,并计算每个子空间内水雾消光性能的建模方法,提高建模的准确性。同时结合红外制导导弹来袭的方向变化,计算出不同方向上的水雾消光性能。从仿真结果可以看出,这种建模方式能够很好地克服以往建模方法的缺陷,为探索水雾的精确建模提供了一种新思路。     

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.     

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     

An integer decomposition algorithm for solving a two‐stage facility location problem with second‐stage activation costs

We study a stochastic scenario‐based facility location problem arising in situations when facilities must first be located, then activated in a particular scenario before they can be used to satisfy scenario demands. Unlike typical facility location problems, fixed charges arise in the initial location of the facilities, and then in the activation of located facilities. The first‐stage variables in our problem are the traditional binary facility‐location variables, whereas the second‐stage variables involve a mix of binary facility‐activation variables and continuous flow variables. Benders decomposition is not applicable for these problems due to the presence of the second‐stage integer activation variables. Instead, we derive cutting planes tailored to the problem under investigation from recourse solution data. These cutting planes are derived by solving a series of specialized shortest path problems based on a modified residual graph from the recourse solution, and are tighter than the general cuts established by Laporte and Louveaux for two‐stage binary programming problems. We demonstrate the computational efficacy of our approach on a variety of randomly generated test problems. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Instant transportation solutions

This paper presents direct noniterative methods for solving such known problems as shoploading and aggregate scheduling. There is given a simple optimal rule for the shop-loading problem which is quite surprising. The crucial point in solving this problem is what not to assign rather than what to assign. The development of those methods was possible since the discussed problems can be converted into a special transportation model where the given cost coefficients c_ij assume a form cij = ui + uj.     

Scheduling for parallel dedicated machines with a single server

This paper examines scheduling problems in which the setup phase of each operation needs to be attended by a single server, common for all jobs and different from the processing machines. The objective in each situation is to minimize the makespan. For the processing system consisting of two parallel dedicated machines we prove that the problem of finding an optimal schedule is N P‐hard in the strong sense even if all setup times are equal or if all processing times are equal. For the case of m parallel dedicated machines, a simple greedy algorithm is shown to create a schedule with the makespan that is at most twice the optimum value. For the two machine case, an improved heuristic guarantees a tight worst‐case ratio of 3/2. We also describe several polynomially solvable cases of the later problem. The two‐machine flow shop and the open shop problems with a single server are also shown to be N P‐hard in the strong sense. However, we reduce the two‐machine flow shop no‐wait problem with a single server to the Gilmore—Gomory traveling salesman problem and solve it in polynomial time. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 304–328, 2000     

On computing the expected discounted return in a markov chain

The discounted return associated with a finite state Markov chain X₁, X₂… is given by g(X₁)+ αg(X₂) + α²g(X₃) + …, where g(x) represents the immediate return from state x. Knowing the transition matrix of the chain, it is desired to compute the expected discounted return (present worth) given the initial state. This type of problem arises in inventory theory, dynamic programming, and elsewhere. Usually the solution is approximated by solving the system of linear equations characterizing the expected return. These equations can be solved by a variety of well-known methods. This paper describes yet another method, which is a slight modification of the classical iterative scheme. The method gives sequences of upper and lower bounds which converge mono-tonely to the solution. Hence, the method is relatively free of error control problems. Computational experiments were conducted which suggest that for problems with a large number of states, the method is quite efficient. The amount of computation required to obtain the solution increases much slower with an increase in the number of states, N, than with the conventional methods. In fact, computational time is more nearly proportional to N², than to N³.