空战机动动作库设计方式研究

具体阐述了两类常见空战动作库的设计 :典型战术动作库和基本操作动作库 ,详细分析了这两类空战机动动作库的不足 ,最后提出了改进动作库设计的具体意见和建议。     

无人机空战仿真中基于机动动作库的决策模型

根据无人机空战仿真需要和专家系统原理,建立了一套基于机动动作库的快速响应自主空战机动决策系统模型;根据第三代战斗机空战中典型的机动动作,建立了相应的专家数据库;通过对驾驶员经验的分析,建立了专家知识库,并将整套决策模型应用于空战仿真中.仿真结果表明,应用该模型建立的空战仿真系统,可以使无人机根据实时战场环境,实现快速自主决策空战模拟飞行.     

自主近距空战中机动动作库及其综合控制系统

针对自主近距空战中机动动作库的设计,采用定性与定量结合的方法对机动动作进行描述,以准确体现机动动作的几何形态和战术意义,构成含描述参数的机动动作库。对于机动动作的控制,提出了按照任务控制-运动学控制-动力学控制-被控对象划分的综合控制系统多层递阶结构。其中,机动动作控制器针对每种机动动作分别设计指令生成器,生成气流角和速率指令;气流角控制器采用含指令滤波的backstepping方法进行控制律设计。以半滚倒转和高速摇摇为例进行仿真验证,仿真结果表明,机动动作的描述参数能准确体现其几何形态和战术意义,并通过综合控制系统实现有效控制。     

21世纪的空战将会怎样?

1995年6月巴黎航展上X-31高机动性验证机的飞行表演震惊了世界航空界.对于其过失速机动能力,国外各种刊物虽然早已有过大量文字报道,但当其真的在千百万观众面前显示多种其他飞机都不能作的令人眼花缭乱的“古怪”动作时,留给人们的印象则是过去任何文字描述都不能比拟的.随后,各种议论也就相继产生.这样的机动动作在空战中能起多大的作用?未来的战斗机是否一定需要这样的能力呢?每当航空装备技术有所突破后,空战的特点即会随之改变.喷气发动机和导弹的应用就是明显的例子.随着中距导弹和雷达技术日趋成熟,空战即演变为两大类:超视距空战和目视格斗空     

空战仿真中机动控制模型研究

针对现代作战飞机方案评估和设计的要求,构建了现代空战仿真系统中计算机控制的飞机仿真基本框架。根据现代空战中典型机动动作对速度矢量的指向要求,从控制飞机速度矢量出发,建立了一类空战机动动作控制的基础模型,并对能反映飞机性能的限制环节进行了建模。仿真结果表明,利用该模型对飞机进行方案评估和性能指标设计是合理的,所建模型能够较好地仿真空战机动,体现飞机性能指标对空战机动的影响。     

基于博弈论的自由空战指挥引导对策问题研究

博弈论是解决空战对抗问题的有效方法之一,针对目前自由空战筹划困难、指挥引导对策单一的现状,在现有空战博弈问题研究的基础上,扩展了机动动作库和控制参数,加入了雷达开机时机这一关键决策,分别从静态博弈和动态博弈角度出发,提出了构建自由空战指挥引导对策模型的思路,为下一步指挥引导对策模型的研究奠定了基础.     

空战机动决策集模型分析

空战机动决策集模型是自主空战机动决策技术的基础,直接影响着空战机动决策的效果。本文综述了应用最为广泛的两类典型空战机动决策集模型,分析了现有基本机动动作决策集模型和典型战术动作决策集模型的特点和不足,提出了机动决策模型的改进方法。     

基于机动动作库的实时轨迹生成与仿真研究

论述了基于机动动作库的实时轨迹生成方法,根据环境和任务要求进行在线决策,选取机动动作库中合适的机动动作,最终实时生成无人机所需要的机动轨迹。仿真结果表明,这种方法具有实时性强、生成轨迹易于跟踪控制等优点。     

灰色关联综合优化法评估超视距空战效能

为克服以往的效能评估中存在效能指标可信度降低、缺乏横向可比性等弱点,提出利用灰色关联综合优化法对战斗机超视距空战效能进行评估.分析影响超视距空战效能的因素,建立超视距空战效能评估层次体系模型,通过评估实例研究利用灰色关联综合优化法评估战斗机超视距空战效能的步骤.通过对评估结果的分析表明,灰色关联综合优化法是评估超视距空战效能的一种有效方法.     

基于兰彻斯特方程的战斗机超视距空战隐身效能分析

随着军事科技的进步,超视距空战逐步成为了现代与未来空战的主要战斗方式.而隐身技术作为提高战斗机空战能力的主要手段之一,正被各国所关注.本文通过分析基于影响图的兰彻斯特微分方程,对战斗机在超视距空战中的隐身效能进行了分析.最后给出算例,计算了不同RCS下的战斗机超视距空战交换比,从而分析了战斗机RCS对战场走势的影响.     

Identification and standardization of maneuvers based upon operational flight data

To find a way of loads analysis from operational flight data for advanced aircraft,maneuver identification and standardization jobs are conducted in this paper. For thousands of sorties from one aircraft, after studying the flight attitude when performing actions, the start and end time of the maneuvers can be determined. According to those time points, various types of maneuvers during the flight are extracted in the form of multi-parameters time histories. By analyzing the numerical range and curve shape of those parameters, a characteristic data library is established to model all types of maneuvers. Based on this library, a computer procedure using pattern-recognition theory is programmed to conduct automatic maneuver identification with high accuracy. In that way, operational loads are classified according to maneuver type. For a group of identified maneuvers of the same type, after the processes of time normalization, trace shifting, as well as averaging and smoothing, the idealization standard time history of each maneuver type is established.Finally, the typical load statuses are determined successfully based on standard maneuvers. The proposed method of maneuver identification and standardization is able to derive operational loads effectively, and might be applied to monitoring loads in Individual Aircraft Tracking Program(IATP).     

先进战斗机过失速机动大气数据融合估计方法

可控的过失速机动是先进战斗机超机动性能的重要标志,飞机飞行包线的扩大已超出传统的大气数据系统测量范围,可靠的迎角、侧滑角、总压、静压等飞行大气数据是制约先进战斗机过失速机动中飞行控制的关键因素。以中国推力矢量验证机为对象,基于过失速机动飞行试验的数据,开展大气参数估计与验证研究。结合过失速机动的时间与空间特性,研究了基于风速、地速、空速矢量和惯性姿态、导航参数的大气参数融合计算方法;针对过失速大迎角状态下飞机周围气流非定常、模型非线性导致的融合大气参数误差的复杂特性,进一步构建深度神经网络,对机动状态融合迎角、侧滑角的强非线性误差进行拟合。仿真和飞行试验表明：该方法可在大迎角飞行状态下实现主要大气参数的融合估计,过失速机动过程中融合迎角误差优于2.3°,融合得到的大气参数可为过失速大迎角机动飞行控制提供可靠的大气参数状态反馈。     

An online ensemble semi-supervised classification framework for air combat target maneuver recognition

Online target maneuver recognition is an important prerequisite for air combat situation recognition and maneuver decision-making. Conventional target maneuver recognition methods adopt mainly supervised learning methods and assume that many sample labels are available. However, in real-world applications, manual sample labeling is often time-consuming and laborious. In addition, airborne sensors collecting target maneuver trajectory information in data streams often cannot process information in real time. To solve these problems, in this paper, an air combat target maneuver recognition model based on an online ensemble semi-supervised classification framework based on online learning, ensemble learning, semi-supervised learning, and Tri-training algorithm, abbreviated as Online Ensemble Semi-supervised Classification Framework (OESCF), is proposed. The framework is divided into four parts: basic classifier offline training stage, online recognition model initialization stage, target maneuver online recognition stage, and online model update stage. Firstly, based on the improved Tri-training algorithm and the fusion decision filtering strategy combined with disagreement, basic classifiers are trained offline by making full use of labeled and unlabeled sample data. Secondly, the dynamic density clustering algorithm of the target maneuver is performed, statistical information of each cluster is calculated, and a set of micro-clusters is obtained to initialize the online recognition model. Thirdly, the ensemble K-Nearest Neighbor (KNN)-based learning method is used to recognize the incoming target maneuver trajectory instances. Finally, to further improve the accuracy and adaptability of the model under the condition of high dynamic air combat, the parameters of the model are updated online using error-driven representation learning, exponential decay function and basic classifier obtained in the offline training stage. The experimental results on several University of California Irvine (UCI) datasets and real air combat target maneuver trajectory data validate the effectiveness of the proposed method in comparison with other semi-supervised models and supervised models, and the results show that the proposed model achieves higher classification accuracy.     

FADS/INS组合法迎角、侧滑角测量方法研究

嵌入式大气数据传感(FADS)系统比传统迎角、侧滑角传感器在测量精度、可靠性、隐身性能上都具有较大的优势,因此该系统可被应用于各型飞行器上。但由于该系统在获得大气参数方面存在延时,所以,它在飞行器机动飞行状态下,迎角和侧滑角的测量精度会下降。针对这一问题,本文提出了以FADS测量结果为基础,采用惯性导航系统(INS)测量的迎角、侧滑角变化量进行修正的FADS/INS组合法迎角、侧滑角测量方法。理论分析和仿真结果表明,该组合系统在飞行器处于平稳和机动飞行时,对迎角、侧滑角的测量均能获得较高精度。     

基于空中发射点的在线诸元计算方法研究

现代战争的快节奏要求弹道导弹具有快速机动发射能力,采用伪卫星组合制导的弹道导弹随机快速发射需要诸元在线计算。提出了一种基于空中发射点的在线诸元计算方法,该方法具有计算量小、制导方法误差小的特点,可用于陆基机动弹道导弹或潜射弹道导弹的快速发射。     

自主空战机动决策方法综述

自主空战机动决策是基于数学优化、人工智能等方法,模拟飞行员空战决策,自动生成飞行控制指令的过程。它在空战仿真、飞行员辅助决策和无人战斗机自主飞行等领域广泛运用。根据求解思路的不同,综合论述了两类自主空战机动决策方法:针对基于对策的空战决策方法,阐述了从追逃对策到双目标对策的发展脉络和内在联系,并着重分析了两类重要的双目标对策模型——矩阵对策和影响图对策;针对基于人工智能的空战决策方法,系统论述了基于专家系统的机动决策、基于遗传学习系统的机动决策、基于人工免疫系统的机动决策和基于神经网络的机动决策,明确了各种决策的建模方法、适用条件、改进途径等问题。总结了各个空战机动决策方法的优点及不足,指出了自主空战机动决策的进一步研究思路。     

A Decision?Directed Adaptive Tracker

In the design of a tracking filter for air traffic control (ATC) applications, a maneuvering aircraft can be modelled by a linear system with random noise accelerations. A Kalman filter tracker, designed on the basis of a variance chosen according to the distribution of the potential maneuver accelerations, will maintain track during maneuvers and provide some improvement in position accuracy. However, during those portions of the flight path where the aircraft is not maneuvering, the tracking accuracy will not be as good as if no acceleration noise had been allowed in the tracking filter. In this paper, statistical decision theory is used to derive an optimal test for detecting the aircraft maneuver; a more practical suboptimal test is then deduced from the optimal test. As long as no maneuver is declared, a simpler filter, based on a constant-velocity model, is used to track the aircraft. When a maneuver is detected, the tracker is reinitialized using stored data, up-dated to the present time, and then normal tracking is resumed as new data arrives. In essence, the tracker performs on the basis of a piecewise linear model in which the breakpoints are defined on-line using the maneuver detector. Simulation results show that there is a significant improvement in tracking capability using the decision-directed adaptive tracker.