基于FAHP-GP的装备调配保障决策模型

分析了装备调配保障决策的主要流程、影响因素和基本模式;根据装备调配保障决策的特点和装备调配保障所面临的实际问题,将模糊层次分析法与目标规划法相结合,构建了装备调配保障决策模型,较好地解决了装备调配保障需求与资源之间的矛盾。最后,以某装备的调配保障决策为例,给出了该模型的求解方法和结果,验证了模型的可行性和有效性。     

基于正则化的多平台多传感器导航定位和探测偏差估计

针对影响舰载雷达探测误差主要为平台导航定位误差和传感器自身探测误差,对这两类误差的影响因素进行了分析,首次综合考虑这两种系统误差,提出了一种基于正则化的多平台导航定位及探测偏差估计方法,利用不同舰艇上传感器对公共目标探测的迭合条件,建立系统误差观测方程,利用LS对两种系统误差同时进行了估计,并在偏差估计中采用正则化估计方法,提高估计结果的稳定性,最后进行了仿真验证,仿真效果验证了算法的有效性,具有较高的工程应用价值。     

第四代歼击机空战战术决策研究

根据第四代歼击机的作战能力,分析了空战过程中可能存在的三种态势,提出了影响第四代歼击机空战战术的重要因素。结合影响因素进行了模糊决策讨论,基于模糊神经网络的方法完成了空战决策系统的设计,并通过神经网络的训练,确定了模糊决策网络。最后,通过仿真验证,表明决策系统实际输出与期望结果是一致的,决策结果是准确的,能够为决策者根据战场态势进行决策提供参考,缩短了决策时间,提高了指挥决策效能。     

变论域模糊自适应滑模有限时间收敛制导律

针对拦截高速机动目标的需求,研究了一种变论域模糊自适应滑模有限时间收敛制导律。推导了导弹-目标空间拦截模型,设计了三维滑模制导律;根据有限时间收敛制导律专家的经验,采用模糊自适应控制方法对滑模制导律的非切换项进行逼近,并设计了有限时间收敛模糊控制规则;提出了一种新型变论域伸缩因子,设计了基于新型伸缩因子的变论域模糊自适应滑模有限时间收敛制导律。仿真结果表明,所设计的制导律能够使导弹准确命中目标,并能够达到视线角速率有限时间收敛,且与比例制导律相比,具有更高的制导精度和更短的飞行时间。     

基于模糊灰关联度的空袭目标属性识别

结合灰关联分析和模糊理论,建立了一种模式识别方法。在此基础上提出了基于模糊灰关联度的空袭目标属性识别模型,利用实例说明了模型的可行性。     

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     

模糊多因素多层次评判在目标威胁度中的应用

目标威胁度评估是目标分配的重要依据,是区分目标的轻、重、缓、急为发射决策提供依据的保证。结合模糊优选理论和AHP法,以某新型地空导弹武器系统为依托,建立相应的威胁度评估模型,为火力单元级自动化作战指挥系统的目标威胁度评估提供了一种简捷有效的方法。     

结合规则知识与神经网络于空袭武器的辅助选择

结合规则推理与神经网络,提出了一种新的空袭武器辅助选择方法。其根据空袭武器选择的基本原则以及问题的特点,构造T-S模糊逻辑系统及其实现的相应神经网络模型,然后通过已有的战例与试验例子训练神经网络,并对问题进行求解,得出空袭武器选择提供依据(一种量化的武器适用度)。另外,还利用专家提供的规则知识于学习实例的预处理,学习实例信息的扩充。     

Cooperative interception with fast multiple model adaptive estimation

For the case that two pursuers intercept an evasive target, the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target, which performs a bang For the case that two pursuers intercept an evasive target, the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target, which performs a bang-bang evasive maneuver with a random switching time. Combined Fast multiple model adaptive estimation (Fast MMAE) algorithm, the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration. Introduced the detection error model related to the line-of-sight (LOS) separation angle of two interceptors, an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance. Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion, Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately. The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.     

基于自适应Kalman滤波算法的航空发动机可测参数及其偏离量估计

针对采用估计可测参数偏离量建立航空发动机机载自适应模型的方案中,可测参数偏离量估计的问题,引入了CA(Constant Acceleration)模型,建立了简化的可测参数状态方程和测量方程,采用自适应Kalman滤波算法直接估计可测参数,由估计出的可测参数与发动机非线性模型计算的额定值之差,获得可测参数偏离量.为解决因简化的状态模型系统误差较大,采用标准Kalman滤波会出现估计严重偏离真值的问题,分析了标准Kalman滤波准则和状态模型误差对滤波结果的影响,采用动态调整状态预报在滤波估计结果中权重的策略,给出了单因子自适应Kalman滤波算法准则及递推公式,使滤波估计准确.对不同的可测参数分别采取序列滤波的方法,减少了运算量.以仿真产生的发动机测量数据为例,对系统模型和所设计的算法进行验证,计算结果表明,所设计的滤波算法具有很快的收敛速度和计算速度,结果优于标准Kalman滤波算法,具有更好的估计精度和一定的工程应用价值.