平衡交通运输网络设计的最优化数学模型

分析了影响交通运输网络发展的因素,总结了课题研究的程序。综合运用了“系统最优原理”和“用户最优原理”,给出了多目标、多运输方式与运送对象的平衡交通网络设计的一般表达式。系统最优与用户最优方法同时又能用于研究国家和省级铁路、公路系统的交通运输问题。文中对这个非凸规划问题给出局部最优解的算法。     

空空导弹的攻击区与截获区

空空导弹的综合性能一贯以“攻击区”表示。现代火控技术采用了离轴瞄准、发射,提出了三种追踪方式(前置、后置及纯追踪),并用“截获区”表示。研讨了“攻击区”“截获区”的确定方法,建立了数学模型,作了算例,讨论了计算结果。     

油膜轴承动态特性参数及转子不平衡的现场识别

 应用有限元方法建立了柔性转子—轴承系统的动力学模型,提出了在不施加人工激励的前提下识别油膜轴承动态特性参数和转子不平衡的一种时—频域混合法。这种方法可以对每个轴承进行单独识别,亦可进行统一识别,真正实现了“现场”和“在线”,而且不破坏油膜的正常工作状态。最后给出了合理的现场识别实验结果。     

基于“虚拟范围”的多机器人围捕算法

 讨论了基于SQL Server数据库的栅格地图建立方法。介绍了一种建立在势场法和栅格地图上的室外多机器人路径规划方法，分析了在已知地图中应用势场法进行路径规划时的路径死点和规划失败问题，并给出了解决方法。以势场栅格法为基础探讨了多机器人协作围捕动态目标的策略。在围捕算法中，根据室外机器人工作环境的特点，提出了“虚拟范围”的概念，以减少动态规划次数，提高围捕速度。介绍了机器人以“虚拟范围”为基准，在各种状态之间的转换机制。利用基于无线局域网的室外多机器人系统进行了试验，试验表明引入“虚拟范围”之后能有效缩短围捕时间，并且存在最短时“虚拟范围”。
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鲁棒的结构网格自动化重叠方法

网格技术是目前数值模拟中的关键技术之一。重叠网格是一种放宽拓扑要求、减小网格生成难度的网格技术。本文以结构重叠网格为基础,分别针对挖洞、寻点以及洞面优化方法进行了研究和改进,同时完成物面网格重叠,形成了一套鲁棒的、自动化的网格重叠系统。在挖洞方面,结合“最小洞映射”方法,提出“复合式挖洞”方法,节省内存开销;在寻点方面,通过构建格心虚网格,保证搜索空间的连续性,同时结合“有效搜索”思想,排除部分对寻点无贡献的网格点,进而减少ADT叉树节点;在洞面优化上,改变填补判别法则并引入两类受保护洞内点,确保两层插值边界建立,提高鲁棒性;在物面网格重叠上,利用物面投影法完成坐标修正,实现物面附近网格流动变量的准确传递。为验证本文方法,分别对定常翼身组合体DLR-F6绕流和非定常机翼挂载分离过程进行了数值模拟,计算结果与实验结果吻合良好,表明该结构重叠网格系统对多物体间定常、非定常扰流具有较好的数值模拟能力和较高的模拟精度,具有较高的工程应用价值。     

大后掠翼大迎角定常与非定常俯仰气动特性及其控制

 对装有“前端襟翼”和“前缘襟翼”的74°后掠三角翼, 在迎角0°～ 90°范围的定常和非定常 (俯仰振动, f = 0. 4, 0. 8, 1. 2Hz) 的空气动力特性以及襟翼的控制作用作了实验研究。定常流的实 验结果与解N 2S 方程的计算结果作了比较。非定常俯仰振动使气动特性的变化出现迟滞现象, 但 仍能保持襟翼对定常流控制所具有的优越性。不装襟翼的三角翼的部分校测结果与文献中相应数 据一致, 表明实验结果可信。     

高超声速飞行器表面测热技术综述

高超声速飞行中飞行器表面气动加热量是飞行器热防护系统最为关键的设计输入,在理论计算与地面模拟的有限近似条件下,通过飞行试验实时获取真实环境下飞行器表面的气动加热量,并在此基础上完成对计算模型与地面试验的验证与改进具有重要意义。详细列举了20世纪50年代以来,国外具有代表性的飞行试验与测热方案。以“内置式”与“嵌入式”作为测热技术的分类特征,介绍了各类测试设备及相应的飞行试验结果。着重分析了“热匹配性”与“结构匹配性”作为关键因素对飞行测热技术的影响,通过飞行试验实例介绍了该问题的解决方法及工程经验。归纳了飞行测热技术发展的共性、特点与未来趋势,并结合当前我国发展现状,对该领域未来研究提出建议。     

遗传算法在雷达吸波涂层多目标优化设计中的应用

本文利用遗传算法实现了雷达吸波涂层的多目标优化设计.首先对目标函数进行合理设计,同时基于Pareto最优概念,采用非劣分层遗传算法(NSGA)进行多目标优化.对具体实例的优化结果表明,该方法利用共享函数和小生境技术,可以使优化结果均匀的收敛于Pareto域附近.同传统优化方法相比,该方法物理意义明确,对决策者来说更具科学性、针对性和实用价值.     

非一致环境中相关信号遥序统计恒虎警检测器及其性能研究

根据有序统计（ＯＳ）理论和恒虚警（ＣＦＡＲ）检测方法，提出了一种非一致环境下瑞利相关信号检测理论和分析方法，并利用上述分析方法对单ＯＳ－ＣＦＡＲ和 大逻辑（ＭＸ）ＯＳ＿ＣＦＡＲ的检测性能进行了分析，试验仿真结果给出了两种检测器在不同环境下的检测性能，有和地验证了上述分析方法的有效性。     

交叉剪切混合层中拟序结构的数值研究

采用拟谱方法对时间模式的交叉剪切混合层进行了直接数值模拟。计算结果表明:与平面混合层一样,展向涡的拉伸作用是交叉剪切混合层中流向涡形成的主要机制。当展向剪切强度较大时(如两主流交叉角为40°),与初期展向KelvinHelmholtz相关的单向旋转流向涡在拉伸作用下很快增长起来,并“坍缩”成“肋状”涡。当交叉角为40°时,涡核区存在类似平面混合层中“方块状”涡的流向涡结构,展向涡辫区还存在一组符号相反的流向涡,不过与“肋状”涡对应的涡结构呈扁平状,始终没有“坍缩”。当交叉角为60°时,“肋状”涡非常强,以致完全抑制了平面混合层“对称模式”的发展。当交叉角小到20°时,流向涡结构更接近于对称分布,然而“肋状”涡却没有形成。另外,计算结果还证实:与二维混合层相比,大强度展向剪切的引入能够加强流场的混合,同时,适当增加展向扰动波初始强度和波数也是提高混合效率的有效手段。     

Distributed CFAR detection in homogeneous and nonhomogeneousbackgrounds

The performance of distributed constant false alarm rate (CFAR) detection with data fusion both in homogeneous and nonhomogeneous Gaussian backgrounds is analyzed. The ordered statistics (OS) CFAR detectors are employed as local detectors. With a Swerling type I target model, in the homogeneous background, the global probability of detection for a given fixed global probability of false alarm is maximized by optimizing both the threshold multipliers and the order numbers of the local OS-CFAR detectors. In the nonhomogeneous background with multiple targets or clutter edges, the performance of the detection system is analyzed and its performance is compared with the performance of the distributed cell-averaging (CA) CFAR detection system     

A new distributed constant false alarm rate detector

A new constant false alarm rate (CFAR) test termed signal-plus-order statistic CFAR (S+OS) using distributed sensors is developed. The sensor modeling assumes that the returns of the test cells of different sensors are all independent and identically distributed In the S+OS scheme, each sensor transmits its test sample and a designated order statistic of its surrounding observations to the fusion center. At the fusion center, the sum of the samples of the test cells is compared with a constant multiplied by a function of the order statistics. For a two-sensor network, the functions considered are the minimum of the order statistics (mOS) and the maximum of the order statistics (MOS). For detecting a Rayleigh fluctuating target in Gaussian noise, closed-form expressions for the false alarm and detection probabilities are obtained. The numerical results indicate that the performance of the MOS detector is very close to that of a centralized OS-CFAR and it performs considerably better than the OS-CFAR detector with the AND or the OR fusion rule. Extension to an N-sensor network is also considered, and general equations for the false alarm probabilities under homogeneous and nonhomogeneous background noise are presented.     

CFAR data fusion center with inhomogeneous receivers

Detection systems with distributed sensors and data fusion are increasingly used by surveillance systems. A system formed by N inhomogeneous constant false alarm rate (CFAR) detectors (cell-averaging (CA) and ordered statistic (OS) CFAR detectors) is studied. A recursive formulation of an algorithm that permits a fixed level of false alarms in the data fusion center is presented, to set the optimum individual threshold levels in the CFAR receivers and the optimum `K out of N' decision rule in order to maximize the total probability of detection. The algorithm also considers receivers of different quality or with different communication channel qualities connecting them with the fusion center. This procedure has been applied to several hypothetical networks with distributed CA-CFAR and OS-CFAR receivers and for Rayleigh targets and interference, and it was seen that in general the fusion decision OR rule is not always the best     

OS-CFAR thresholding in decentralized radar systems

In a decentralized detection scheme, several sensors perform a binary (hard) decision and send the resulting data to a fusion center for the final decision. If each local decision has a constant false alarm rate (CFAR), the final decision is ensured to be CFAR. We consider the case that each local decision is a threshold decision, and the threshold is proportional, through a suitable multiplier, to a linear combination of order statistics (OS) from a reference set (a generalization of the concept of OS thresholding). We address the following problem: given the fusion rule and the relevant system parameters, select each threshold multiplier and the coefficients of each linear combination so as to maximize the overall probability of detection for constrained probability of false alarm. By a Lagrangian maximization approach, we obtain a general solution to this problem and closed-form solutions for the AND and OR fusion logics. A performance assessment is carried on, showing a global superiority of the OR fusion rule in terms of detection probability (for operating conditions matching the design assumptions) and of robustness (when these do not match). We also investigate the effect of the hard quantization performed at the local sensors, by comparing the said performance to those achievable by the same fusion rule in the limiting case of no quantization     

Detection loss due to interfering targets in ordered statisticsCFAR

The ordered-statistics (OS) constant false-alarm rate (CFAR) is relatively immune to the presence of interfering targets among the reference cells used to determine the average background. OS CFAR performance in a multitarget environment was previously studied by simulation. The author obtains analytic expressions for the added detection loss, assuming strong interfering targets. The real target is assumed to be a Rayleigh fluctuating target. Numerical examples are included     

Coverage area analysis for decentralized detection in Weibullclutter

The performance of a decentralized constant false-alarm rate (CFAR) detection system with data fusion in homogeneous non-Gaussian background is analyzed in terms of ground area covered. The advantages of using a distributed radar system and the differences between the system behavior in Rayleigh clutter and in Weibull clutter are stressed. Notably, the increasing benefit of cooperative decision making when clutter becomes spikier is pointed out     

Analysis of some modified ordered statistic CFAR: OSGO and OSSOCFAR

It is necessary for automatic detection radars to be adaptive to variations in background clutter in order to maintain a constant false alarm rate (CFAR). A CFAR based on an ordered statistic technique (OS CFAR) has some advantages over the cell-averaging technique (CA CFAR), especially in clutter edges or multiple target environments; unfortunately the large processing time required by this technique limits its use. The authors present two new OS CFARs that require only ahlf the processing time. One is an ordered statistic greatest of CFAR (OSGO), while the other is an ordered statistic smallest of CFAR (OSSO). The OSGO CFAR has the advantages of the OS CFAR with only a negligible increment to the CFAR loss     

Data quantization effects in CFAR signal detection

In this paper, we investigate data quantization effects in constant false alarm rate (CFAR) signal detection. Exponential distribution for the input data and uniform quantization are assumed for the CFAR detector analysis. Such assumptions are valid in the case of radar for a Swerling I target in Gaussian clutter plus noise and a receiver with analog square-law detection followed by analog-to-digital (A/D) conversion. False alarm and detection probabilities of the cell averaging (CA) and order statistic (OS) CFAR detectors operating on quantized observations are analytically determined. In homogeneous backgrounds with 15 dB clutter power fluctuations, we show analytically that a 12-bit uniform quantizer is sufficient to achieve false alarm rate invariance. Detector performance characteristics in nonhomogeneous backgrounds, due to regions of clutter power transitions and multiple interfering targets, are also presented and detailed comparisons are given     

Analysis of CFAR processors in homogeneous background

Five different constant false alarm rate (CFAR) radar processing schemes are considered and their performances analyzed in homogeneous and nonhomogeneous backgrounds, the latter specifically being the multiple target environment and regions of clutter transitions. The average detection threshold for each of the CFAR schemes was computed to measure and compare the detection performance in homogeneous noise background. The exponential noise model was used for clear and clutter backgrounds to get closed-form expressions. The processor types compared are: the cell-averaging CFAR, the `greatest of' CFAR, the `smallest of' CFAR, the ordered-statistics CFAR, and a modified ordered-statistics processor called the trimmed-mean CFAR     

Analysis of an adaptive CFAR detector in non-Gaussian interference

Coherent signal detection in non-Gaussian interference is presently of interest in adaptive array applications. Conventional array detection algorithms inherently model the interference with a multivariate Gaussian random vector. However, non-Gaussian interference models are also under investigation for applications where the Gaussian assumption may not be appropriate. We analyze the performance of an adaptive array receiver for signal detection in interference modeled with a non-Gaussian distribution referred to as a spherically invariant random vector (SIRV). We first motivate this interference model with results from radar clutter measurements collected in the Mountain Top Program. Then we develop analytical expressions for the probability of false alarm and the probability of detection for the adaptive array receiver. Our analysis shows that the receiver has constant false alarm rate (CFAR) performance with respect to all the interference parameters. Some illustrative examples are included that compare the detection performance of this CFAR receiver with a receiver that has prior knowledge of the interference parameters