两部雷达部署的研究

本文研究了两部雷达联合发现目标概率 ,采用特征尺度的分析方法解决了最佳部署问题 ,得到了极其简单 ,计算量很小的关于确定最佳距离的有效公式 ,并用实际算例进行了验证     

多部雷达最佳部署的遗传算法研究

本文研究了多部雷达联合发现目标概率 ,并采用 Monte Carlo模拟计算面积和遗传算法寻优相结合来计算多部雷达最佳部署 .文中给出了多部雷达部署的一般公式 ,并对雷达数目为 2、3、4的情况进行了计算     

基于相似度和改进雷达图的概率犹豫模糊多属性决策方法

为了解决属性权重未知的概率犹豫模糊多属性决策问题,构建基于相似度和改进雷达图的多属性决策方法。首先基于概率犹豫模糊元的信息完全度构建概率犹豫模糊集的距离测度和灰色关联系数,然后基于灰色关联理想解法定义了概率犹豫模糊集的相似度,最后根据概率犹豫模糊集的相似度和改进雷达图构建多属性决策模型,并通过案例进行了验证分析。     

基于直觉模糊熵权和CC-OWA算子的雷达目标识别模型

为更完整的描述和表达雷达目标类型识别中的目标特征和目标类型之间的关系复杂性和知识缺乏性,通过直觉模糊关系描述,进而将目标识别特征信息转化为直觉模糊集信息.分析了基于直觉模糊集理论的雷达目标类型识别知识建模,揭示了直觉模糊信息的价值可以通过直觉模糊熵刻画,进而提出应用直觉模糊集的熵构造特征直觉模糊信息的权重(直觉模糊熵权),充分利用了目标类型识别知识中隐含的权重信息,并结合CC-OWA算子建立雷达目标类型识别模型与识别步骤,利用一个雷达目标识别实例说明了模型的有效性.     

舰船运动航迹相关临界度的仿真模型

以雷达航迹与红外线角度航迹的相关为例 ,分析舰船运动航迹相关的临界度指标 ,建立状态方程 ,进行相应的运算和统计计算 ,并给出准确的仿真输出结果 .     

Refined Analysis of Sparse MIMO Radar

We analyze a multiple-input multiple-output (MIMO) radar model and provide recovery results for a compressed sensing (CS) approach. In MIMO radar different pulses are emitted by several transmitters and the echoes are recorded at several receiver nodes. Under reasonable assumptions the transformation from emitted pulses to the received echoes can approximately be regarded as linear. For the considered model, and many radar tasks in general, sparsity of targets within the considered angle-range-Doppler domain is a natural assumption. Therefore, it is possible to apply methods from CS in order to reconstruct the parameters of the targets. Assuming Gaussian random pulses the resulting measurement matrix becomes a highly structured random matrix. Our first main result provides an estimate for the well-known restricted isometry property (RIP) ensuring stable and robust recovery. We require more measurements than standard results from CS, like for example those for Gaussian random measurements. Nevertheless, we show that due to the special structure of the considered measurement matrix our RIP result is in fact optimal (up to possibly logarithmic factors). Our further two main results on nonuniform recovery (i.e., for a fixed sparse target scene) reveal how the fine structure of the support set—not only the size—affects the (nonuniform) recovery performance. We show that for certain “balanced” support sets reconstruction with essentially the optimal number of measurements is possible. Indeed, we introduce a parameter measuring the well-behavedness of the support set and resemble standard results from CS for near-optimal parameter choices. We prove recovery results for both perfect recovery of the support set in case of exactly sparse vectors and an \(\ell _2\)-norm approximation result for reconstruction under sparsity defect. Our analysis complements earlier work by Strohmer & Friedlander and deepens the understanding of the considered MIMO radar model. Thereby—and apparently for the first time in CS theory—we prove theoretical results in which the difference between nonuniform and uniform recovery consists of more than just logarithmic factors.     

Scheduling for a multifunction phased array radar system

A multifunction radar is a new, complex, radar system which combines the previously isolated tasks of searching volumes of space, tracking targets and guiding missiles. This study was instigated by the Defence Research Agency who require scheduling rules for their newly developed multifunction radar system. The primary interest when looking at the operational efficiency of this type of radar system is to schedule the radar jobs effectively. These jobs take the form of a coupled task which consists of two distinct operations that require processing in a predetermined order and at a specified interval apart. In the radar scenario, each job is cyclic in nature with its own due date and processing time. The need for an on-line scheduler restricts the radar controller to use heuristic methods. A detailed functional simulation model, which generates a multifunction radar environment, has been developed to aid the evaluation of the various scheduling heuristics that we have proposed.     

Multivariate elliptically contoured stable distributions: theory and estimation

Stable distributions with elliptical contours are a class of distributions that are useful for modeling heavy tailed multivariate data. This paper describes the theory of such distributions, presents formulas for calculating their densities, and methods for fitting the data and assessing the fit. Efficient numerical routines are implemented and evaluated in simulations. Applications to data sets of a financial portfolio with 30 assets and to a bivariate radar clutter data set are presented.     

A Characterization of Model Multi-colour Sets

Model sets are always Meyer sets, but not vice versa. This article is about characterizing model sets (general and regular) amongst the Meyer sets in terms of two associated dynamical systems. These two dynamical systems describe two very different topologies on point sets, one local and one global. In model sets these two are strongly interconnected and this connection is essentially definitive. The paper is set in the context of multi-colour sets, that is to say, point sets in which points come in a finite number of colours, that are loosely coupled together by finite local complexity. Communicated by Jean Bellissard submitted 8/10/04, accepted 15/02/05     

多雷达系统数据融合与航迹预测

为了便于准确定位传感器网络,分布式的单雷达系统首先进行各自的数据处理,包括:地理坐标换算至平面直角坐标;剔除孤立的异常点迹;采用模糊c-均值聚类方法和"动态分区",将单雷达数据中属于同目标的相似点迹归类集合;根据雷达观测和目标运动的特征,在每个点迹集合中设计门限滤波和相关矩阵检验,提取完整连续的目标运动的航迹;结合各航迹特征进行种类分析.接下来对属于不同雷达的航迹两两比较,找出有相交时间段的航迹,采用三次样条对两条航迹的进行内插和外推,再通过模糊综合函数对这两条航迹给出一个相似性度量,并取阈值为0.85.最后得出雷达间各航迹匹配关系.通过该雷达所观测到的航迹的稳定程度来近似估计其观察精度.首先对每一条航迹进行分段拟合得到其剩余方差,然后直接用每一条航迹的剩余方差来衡量雷达的观察精度,最后我们得出雷达的精度排序29107728,7724253720252539.对航迹融合,我们首先采用D-S证据理论并利用分析得到的雷达精度,对表示同一目标的航迹对进行融合.其次试图运用卡尔曼滤波对航迹进行融合:思路一是设法离线估计出噪声矩阵,得出系统噪声方差矩阵和观测噪声方差矩阵,从而用于标准卡尔曼滤波方程;思路二是探究较为实用的自适应滤波,兼顾Sage-Husa自适应滤波算法的高精度与强跟踪自适应滤波算法的可靠性,采用了一种混合算法给出收敛的估计.最终给出了雷达7728和2910的融合算例以及10秒钟的预测轨迹.最后,我们将导弹拦截飞机建模为三维的追逃问题,建立了运动学关系方程,最终归结为最小能量导引律问题.采用"模糊T-S线性模型"以及RH控制方法和伴随技术,在目标作对抗性机动条件下,获得了一个有效拦截的导引律.还对多雷达系统平均处理周期、数据融合系统的航迹处理周期进行了分析,对雷达网络实时性做了评价.