具有战时随机延误与损耗的多配送中心路径优化

建立了具有战时随机延误与损耗的多配送中心配送路径安排模型,给出了基于随机模拟的蚁群算法。算法通过给定残存率、用时与置信度阈值,把多目标问题作为单目标来处理。用随机模拟的方法来求路径的置信度,并以此为基础搜索转移策略的临域与判断未遍历点的插入位置。算法设计了符合问题特点的从虚拟点出发的转移策略与对两类路段不同的信息素更新策略,确保算法的实现。最后,通过算例说明了该方法的可行性与有效性。     

基于D-S证据理论的技术成熟度评估关键技术识别方法研究

针对技术成熟度评估中识别关键技术这一多属性群决策问题,提出了采用证据理论的一种新方法,解决了由于专家意见具有发散性和冲突性而导致的意见难以合成的问题．通过引入和分析不确定度的概念和含义,给出新的不确定度的计算方法,有效地解决了评价中的证据冲突问题．最后通过一个算例给出了应用的步骤和方法．     

基于滤波与BP神经网络的水下探测信号检测算法

针对水下探测系统探测船舶磁场信号时信噪比较低的问题,首先根据磁异常信号的频域特征,设计了约束最小二乘FIR滤波器,通过对含噪信号进行带通滤波,滤除高频噪声;再采用BP神经网络对低频分量进行学习,提取船舶目标特征信号。将该算法应用于船模实测实验,结果表明:该算法可以显著提高信噪比,增强对船舶磁场信号的检测能力。     

基于Kalman预测的自适应线性化信号的生成方法

自旋弹体的姿态通常采用单通道控制方法进行控制。该方法中采用线性化信号对控制信号进行调制,而线性化信号采用固定频率随机相位的信号发生器产生,这就会对弹体控制产生不良的影响。提出了一种基于Kalman预测的自适应线性化信号的实现方法。该方法通过弹旋频率测定、频率的Kalman预测和信号发生器实现了线性化信号对弹旋信号的同步、自适应倍频、定步长、定幅度的信号发生功能。仿真实验证明了方的正确性,为更加合理的控制自旋导弹的姿态提供了一种有效的方法。     

基于信号仿真支撑的角度欺骗功能仿真方法

根据雷达信号仿真和功能仿真各自的优缺点,提出了一种基于信号仿真支撑的雷达角度欺骗干扰功能仿真方法。在介绍了雷达系统仿真的两种基本形式后,提出了雷达欺骗干扰功能仿真的基本思想。以交叉极化角度欺骗干扰为例,提出了雷达欺骗干扰功能仿真关键模型的建立方法,并详细说明了其实现步骤。最后进行了仿真验证,分析了功能仿真和信号仿真精度和仿真用时的差别,结果表明了所提方法的可行性和有效性。     

多机头螺杆机组的优化运行控制

风冷式冷水机组的能效较低,为了提高机组性能,机组被设计成具有2个或多个制冷剂回路的多机头形式。对多机头风冷式冷水机组的运行控制进行了研究,探讨了其优化运行控制策略,利用TRNSYS软件建立了机组模型并进行了验证,最后利用典型的办公建筑来评估采用优化运行控制策略后机组的节能潜力。同常规控制策略相比,单纯优化运行制冷剂回路的控制策略能使机组年度能耗下降6.5%,同时优化运行制冷剂回路和压缩机的控制策略能使机组年度能耗下降7.9%,节能效果显著。     

中远距协同空战多目标攻击决策

根据先进战斗机的技术特点和中远距协同空战发展方向,提出一种基于先敌发现、先敌发射、先敌摧毁能力的空战态势分析模型。同时,将并行遗传算法与分布估计算法相结合的并行分布遗传算法应用于上述模型,给出了求解多目标攻击决策问题的算法。最后利用具体算例进行仿真验证。结果表明空战态势分析模型能够较为准确地描述中远距多机协同空战,采用的算法具有较好的可行性和实时性,为中远距多机协同空战的多目标攻击决策提供了新的思路和方法。     

脉压雷达信号处理仿真分析研究

针对脉冲压缩雷达,从系统仿真的角度建立了描述雷达与目标位置的三维坐标系,根据雷达信号脉冲压缩和非相参积累模型,通过计算机Matlab仿真得出了目标回波经过脉压雷达信号处理后的结果,并进一步分析了脉冲压缩和脉冲积累处理的效能。结果表明:目标回波信号通过脉冲压缩和脉冲积累处理后,显著提高了目标回波信噪比以及对目标距离估计的精度。     

基于联合时频滤波的多目标回波信号分离算法

针对弹道中段多目标回波信号分离的问题,提出了基于联合时频滤波的多目标信号分离算法。该算法先对多目标回波信号进行自相关处理和峰值提取,得到目标信号的周期,接着用该周期对回波信号的Gabor变换和PWVD变换结果进行分段,然后将各段时频图相乘,获得新的时频支撑域,最后用新的时频支撑域对回波信号进行时频滤波,克服了交叉项的干扰和时频聚集性不高的缺点,分离出了不同周期的目标信号。仿真结果证明了该方法的有效性和先进性。     

Signal‐to‐interference‐plus‐noise ratio estimation for wireless communication systems: Methods and analysis

The Signal‐to‐Interference‐plus‐Noise Ratio (SINR) is an important metric of wireless communication link quality. SINR estimates have several important applications. These include optimizing the transmit power level for a target quality of service, assisting with handoff decisions and dynamically adapting the data rate for wireless Internet applications. Accurate SINR estimation provides for both a more efficient system and a higher user‐perceived quality of service. In this paper, we develop new SINR estimators and compare their mean squared error (MSE) performance. We show that our new estimators dominate estimators that have previously appeared in the literature with respect to MSE. The sequence of transmitted bits in wireless communication systems consists of both pilot bits (which are known both to the transmitter and receiver) and user bits (which are known only by the transmitter). The SINR estimators we consider alternatively depend exclusively on pilot bits, exclusively on user bits, or simultaneously use both pilot and user bits. In addition, we consider estimators that utilize smoothing and feedback mechanisms. Smoothed estimators are motivated by the fact that the interference component of the SINR changes relatively slowly with time, typically with the addition or departure of a user to the system. Feedback estimators are motivated by the fact that receivers typically decode bits correctly with a very high probability, and therefore user bits can be thought of as quasipilot bits. For each estimator discussed, we derive an exact or approximate formula for its MSE. Satterthwaite approximations, noncentral F distributions (singly and doubly) and distribution theory of quadratic forms are the key statistical tools used in developing the MSE formulas. In the case of approximate MSE formulas, we validate their accuracy using simulation techniques. The approximate MSE formulas, of interest in their own right for comparing the quality of the estimators, are also used for optimally combining estimators. In particular, we derive optimal weights for linearly combining an estimator based on pilot bits with an estimator based on user bits. The optimal weights depend on the MSE of the two estimators being combined, and thus the accurate approximate MSE formulas can conveniently be used. The optimal weights also depend on the unknown SINR, and therefore need to be estimated in order to construct a useable combined estimator. The impact on the MSE of the combined estimator due to estimating the weights is examined. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004