机载雷达BIT设计研究

首先分析了机载雷达自检测(BIT)采用的设计思想;然后论述了机载雷达自动实施的加电自检(PBIT)、连续自检(CBIT)、人工干预的中断自检(IBIT)和快速自检(QBIT);最后给出机载雷达BIT信息处理的流程.     

遗传算法在弹道数据融合中的应用

目前靶场采用全测元融合滤波的方式确定目标飞行器弹道,为测控设备提供引导信息并为安全决策和指挥显示提供数据支持。随着靶场测控设备种类和数量越来越丰富,而新的试验任务又对实时弹道融合解算精度提出了更高的要求,全测元融合处理能否提供高精度的融合弹道和快速地从海量融合方案中寻找最优的方案是弹道测量数据融合急需解决的问题。本文提出了基于遗传算法的最优融合方案选择方法,利用该方法得到的融合弹道精度优于全测元融合方案,仿真结果表明了方法的有效性。     

基于决策不确定性的多目标跟踪传感器管理

针对高杂波、电子干扰环境,在量测驱动的多目标滤波框架下提出了一种基于决策不确定性的传感器管理方法。首先,根据部分可观测马尔科夫决策过程的理论,给出了基于Rényi信息增量的传感器管理一般方法。其次,综合考虑决策过程的信息完整性、信息质量、信息的内涵等因素,在量测驱动的自适应滤波框架下,基于目标运动态势评估多目标决策不确定性水平,并选取最大决策不确定性目标。最后,以最大决策不确定性目标的信息增量最大化为准则进行传感器分配方案的求解。仿真实验表明所提方法能够有效抑制电子干扰、杂波对多目标跟踪及传感器分配的影响,与基于威胁的传感器管理方法相比,所提方法的平均最优子模式分配（OSPA）距离及平均计算时长均显著降低,且在高杂波、电子干扰情形下具有较高的可靠性。     

一种适用于现代飞机火控系统的空-空导弹发射火控计算数学模型

给出的火控计算模型由两部分构成：首先利用机载雷达和其它传感器提供的信息,求解目标速度Vt和进入角Q,然后采用表格函数线性插值与解析修正相结合的方法,来求解导弹发射的最大、最小允许距离。最后给出了仿真结果。该数学模型已经用于某现役歼击机改装的雷达火控系统。     

不确定环境下的无人机攻击策略研究

给出了一种在不确定环境下的无人机攻击决策分析方法。首先分析机载雷达的作战性能,得到正确判别敌方目标和错误判别敌方目标二者之间的关系;然后建立了一种基于Lanchester方程的诱饵弹干扰下的无人机攻击决策模型,进而表示出目标识别准确度对作战进程的影响;随后通过对Lanchester方程积分,得到了真目标损耗和假目标损耗二者之间的函数关系,从而完成对Lanchester方程的化简;最后采用差分进化算法来进行无人机的攻击决策优化,并且通过作战仿真验证了该方法的有效性。     

船载星敏感器测星数据蒙气差实时修正方法

针对船载星敏感器安装在航天测量船上而引起的测星数据如何进行蒙气差实时修正问题,在分析船载经纬仪目前使用的蒙气差修正方法和中国天文年历提供的蒙气差修正方法的修正精度的基础上,提出船载星敏感器测星数据的蒙气差实时修正方法,给出蒙气差常数R0和温度变差乘数A的改正量α的计算公式,解决了工程应用上的高精度和实时性问题;同时,在分析大气温度、大气压力对船载星敏感器测星数据蒙气差影响的基础上,提出工程应用中的气象数据采集与使用的具体方案。     

联合截获威胁下的雷达射频隐身目标搜索算法

针对基于射频(RF)隐身需求的机载雷达目标搜索问题进行了研究。通过分析实际作战中射频辐射面临的截获威胁,提出了一种基于联合截获威胁的射频隐身性能表征方法,并给出了具体的值估算方法;对机载雷达搜索任务中辐射参数的优化问题进行了建模,并采用优化算法对目标函数进行求解。通过分析典型情景下解集的分布特点,给出了从最优解集中选取最终解的方法。结果表明,提出的射频隐身性能表征方法能更好地反映截获实现过程的多域需求,提出的雷达搜索方法能够在保证探测性能的同时,提高射频隐身性能和搜索速度,能够为相控阵雷达搜索任务中参数的优化控制提供方法和依据。     

机载雷达岛岸背景舰船识别能力研究

本文介绍了机载雷达目标识别技术的现状,以及现有机载雷达所采取的目标识别技术,并用试验验证了机载雷达对岛屿与小型船只的分类能力。说明应用步进频体制可实现对目标与背景的高分辨距离成像,通过该体制可提取出目标的距离维尺度特征。该技术可用于区分海域中的岛礁、小型舰艇、大型舰船等尺度差异较大的目标与背景。     

地空协同防空目标抗差跟踪算法

针对现有系统误差配准算法以已知系统误差变化模型为前提条件、相应的目标状态估计易受系统误差配准结果影响等不足之处,在机载雷达与地基雷达协同防空预警体系下,对系统误差存在情况下的目标跟踪问题进行了研究,并提出了有效的地空协同防空目标抗差跟踪算法。仿真结果表明所提算法可得到无偏、稳定、有效的目标状态估计,并且相对于系统误差目标状态联合估计算法,所提算法计算量小,对系统误差变化有很强的鲁棒性,可适应实际工程应用中可能出现的异常情况,为后续决策提供稳定有效的目标信息。     

类似系统可靠性信息在复杂系统Bayes可靠性评估中的应用

先验分布的确定是运用Bayes方法进行复杂系统可靠性评估的先决条件。本文研究了利用类似系统的可靠性信息通过加权融合确定先验分布的方法。对Weibull和指数分布模型的可靠性试验结果,给出Bayes方法可靠性计算公式。最后给出的一个实例表明,该方法计算简便、便于工程应用。      

The Series 320 Radar: Three-Dimensional Air Surveillance for the 1980s

Air surveillance radars for this decade will be required to provide reliable target location and trajectory information in height as well as the conventional geographical coordinates. These threedimensional radars will perform this task in spite of adverse environmental conditions such as ground, airborne clutter, and electromagnetic interference. The use of powerful false-alarm control processing allows automatic target detection and remoting of target information without overloading central processing capabilities. The technological evolution of the past decade has allowed sophisticated analysis, antenna/receiver/transmitter design, and signal/data processing techniques to be applied to the next generation of practical production radar systems. These radars will meet more severe performance requirements and will be significantly improved in terms of reliability, maintainability, and life cycle cost considerations. A candidate radar to fulfill the air surveillance role of this decade is the Series 320 radar manufactured by ITT Gilfillan.     

Modeling and Estimation for Tracking Maneuvering Targets

A new approach to the three-dimensional airborne maneuvering target tracking problem is presented. The method, which combines the correlated acceleration target model of Singer [3] with the adaptive semi-Markov maneuver model of Gholson and Moose [8], leads to a practical real-time tracking algorithm that can be easily implemented on a modern fire-control computer. Preliminary testing with actual radar measurements indicates both improved tracking accuracy and increased filter stability in response to rapid target accelerations in elevation, bearing, and range.     

基于机载单天线雷达一维距离数据的地表三维信息优化重建方法

陆地的刚体表面在机载雷达的任意相对运动下具有几何不变性,基于此约束可利用雷达距离像中提取出的地面多个强散射点的一维距离数据,重建出地表的三维信息以及载机未知的运动轨迹.鉴于现有基于雷达远场假设的重建方法无法适用于具有较大近场误差的地面目标重建的问题,提出一种基于雷达近场几何模型的优化重建方法,采用非线性优化方法实现了对...     

Radar Altimeter Optimization for Geodesy Over the Sea

The optimum processor and its accuracy limit for radar altimetry for geodetic use over the sea are studied with a model accounting for random surface reflectivity, sea height variation, additive noise, and pointing errors, and allowing for arbitrary antenna patterns, signal modulations, and other system parameters. The ?threshold? case solution (which can have any specified accuracy) dictates a signal modulation bandwidth just shy of resolving the sea height variation and/or illuminated sea area (as scaled into time delay and ?smeared? by pointing errors). For such a modulation a relatively complete solution is obtained. These results are used to determine practical radar altimeter designs, additionally accounting for antenna size, stability, and peak power restraints. Conditions allowing neglecting of limiting or complicating effects due to temporally varying reflectivity, sea height, and vehicle position are given and shown to be satisfied for a typical satellite.     

Development of airborne moving target radar for long range surveillance

The following topics are discussed in the context of the development of an airborne moving target radar for long range surveillance: US Navy long range shipborne radar; Cadillac I airborne early warning (AEW) radar; Cadillac II airborne early warning (AEW) radar; airborne moving target indicating (AMTI) radar; related post-war radar activities; and the invention of the displaced center antenna. Among the topics studied is the use of a monopulse antenna in an MTI radar to remove the degradation of the MTI caused by rapid scanning of the antenna. A method of using a monopulse antenna for motion compensation in airborne MTI is discussed.<>     

Tracking with classification-aided multiframe data association

In most conventional tracking systems, only the target kinematic information from, for example, a radar or sonar or an electro-optical sensor, is used in measurement-to-track association. Target class information, which is typically used in postprocessing, can also be used to improve data association to give better tracking accuracy. The use of target class information in data association can improve discrimination by yielding purer tracks and preserving their continuity. In this paper, we present the simultaneous use of target classification information and target kinematic information for target tracking. The approach presented integrates target class information into the data association process using the 2-D (one track list and one measurement list) as well as multiframe (one track list and multiple measurement lists) assignments. The multiframe association likelihood is developed to include the classification results based on the "confusion matrix" that specifies the accuracy of the target classifier. The objective is to improve association results using class information when the kinematic likelihoods are similar for different targets, i.e., there is ambiguity in using kinematic information alone. Performance comparisons with and without the use of class information in data association are presented on a ground target tracking problem. Simulation results quantify the benefits of classification-aided data association for improved target tracking, especially in the presence of association uncertainty in the kinematic measurements. Also, the benefit of 5-D (or multiframe) association versus 2-D association is investigated for different quality classifiers. The main contribution of this paper is the development of the methodology to incorporate exactly the classification information into multidimensional (multiframe) association.     

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.     

基于IMU的机载TOPSAR目标定位方法

Burst工作模式和方位波束的主动扫描使得TOPSAR工作模式能够有效削弱ScanSAR模式的扇贝效应,同时也导致图像方位向像素位置与回波脉冲的关系变得复杂,给目标定位带来了很多问题。惯性测量单元(IMU)数据记录延时也会导致方位向定位误差存在,精确估计这个误差能够大大提高目标的方位向定位精度。从TOPSAR数据采集的几何关系和成像过程出发,结合机载IMU数据,提出了一种新的机载TOPSAR目标定位方法。该方法能够直接从TOPSAR斜距图像中获取目标的经纬度信息。通过实际飞行试验获取的机载TOPSAR数据验证了该方法的有效性,能够获取25 m的平均定位精度。     

基于高度分集的两波束米波雷达测高方法及其应用

米波雷达的波束较宽、由于地面反射引起波瓣分裂,通常只能估高而不能用来测高.针对这一难题本文提出一种基于高度分集的两波束米波雷达测高方法.该方法采用高度不同的两个天线,利用波瓣分裂情况及相互相位关系来测量目标高度.文章分析了此方法测高的精度及影响精度的一些因素.本测高方法已应用于某型雷达信号处理机中,并取得良好效果.     

基于概率分析法的歼击机导引效能

 基于传统的概率分析法提出一种评估歼击机导引效能的方法。以发现距离作为整个导引阶段的随机变量,提出导引效能指标。将歼击机在水平面内的航向导引分为远距导引与近距导引,分别把导航站引起的导引误差、歼击机对目标的拦截角作为随机变量,分析歼击机进入机载雷达发现区和武器攻击区的情况。通过计算机载雷达的累积发现概率给出发现距离函数。最后以某型歼击机使用中程空空导弹拦截目标为例,计算歼击机向目标的成功导引概率、有效导引距离及有效导引角度,分析主要参数对导引概率的影响规律。该方法可应用于歼击机空战时导引概率的计算。