基于改进型CFAR的SAR图像目标检测算法

针对传统的双参数恒虚警率(Constant False Alarm Rate,CFAR)算法存在的虚警率高、实现过程繁琐、算法执行效率低等问题,提出了一种改进型的CFAR检测算法。该算法根据SAR图像的统计直方图,对可疑的目标像素进行预筛选,再用2个滑动窗口对像素进行判别。改进型的CFAR检测算法简化了原检测算法的结构,降低了检测结果的虚警率,提高了算法的计算效率,并在国际公开的雷达数据集上进行软件与DSP硬件的应用验证,测试表明该算法的有效性。     

三维战术训练空域规划方法研究

三维战术训练空域规划是对空域的合理规划利用,人工排样已经无法保证空域的安全有效利用,寻找高效快速的算法对于提高空域利用率和训练效率具有重要意义。通过对空域规划的理论研究,结合空域特性,将整个空域划分为立方体单元,构建相应的训练空域规划模型;在此基础之上,利用改进的遗传算法来寻求最优方案,剔除了大量不可行解;通过实例仿真验证该空域规划模型的合理性和有效性。结果表明:该空域模型是合理和有效的,提高了空域规划的速度和准确度。     

人工智能赋能空域系统,提升空域分层治理能力

为应对未来平台无人化、用户多元化、服务个性化的航空发展趋势,空域运行将呈现分层治理的发展趋势。而随着算力、算法与数据的持续提升与积累,数据驱动的人工智能方法将持续为层次化的空域系统赋能。本文从超低空、城市、区域、枢纽、亚轨道等5个层次化场景梳理我国空域系统的发展趋势;提炼得出空域运行面临的核心难点与关键科学问题;提出基于数据驱动的人工智能方法求解空域运行科学问题的研究框架、研究内容和应关注的关键技术;结合层次化典型应用场景,简要分析智能空域运行的具体应用案例;最后对人在空域运行中的作用提出了新的思考。     

机载共形阵天线阵列信号处理的关键技术分析

机载共形阵是机载有源相控阵天线发展的下一个目标。共形阵天线的阵元具有不同的方向性,空间分辨率算法必须考虑来波的极化问题。本文研究了空间信号波达方向(Direction of Arrival,DOA)估计算法的现状,分析了一种适用于共形阵天线空域自适应波束形成的高速滤波算法。     

基于改进灰狼优化算法的电子干扰机空域划设

在现代化战争中,遂行突防任务的战斗机通常需要与电子干扰机协同配合,来完成特定的任务。为了更高效地利用空域资源,针对突防作战中干扰机的最优空域划设问题,在给出电子干扰下雷达探测范围、航线安全间隔以及有效干扰时间的基础上,建立电子干扰机的最优空域划设模型;在原始灰狼算法中将线性调节参数改为非线性调节参数,同时引入粒子群算法中的记忆功能,改进原始灰狼算法;并采用改进的灰狼优化算法寻找最优的空域基准点,进而确定电子干扰机的最优空域范围。结果表明:对于特定的突防任务,改进的灰狼优化算法比原始灰狼优化算法和粒子群算法更具优越性,能够快速、高效地找到最优的空域基准点;本文所建立的电子干扰机的最优空域划设模型是科学、有效的。     

训练空域动态规划问题数值模拟仿真算法研究

训练空域的动态规划对于提高空域利用率,提高部队训练效率,缓解军民用空矛盾具有重要意义。本文将空域的动态规划问题进行分阶段处理,通过寻求各个阶段的最优方案来使得总的占用时间最短。针对各个阶段的动态规划问题,在分析问题复杂性的基础上,构建了空域规划模型,提出了遗传-离散粒子群算法,通过融合遗传算法中的交叉与变异思想来改善DPSO算法摆脱局部最优解的能力,提高算法的收敛速度和精度。同时为保证种群的多样性,设计了可保证个体可行性的自适应交叉算子和变异算子。最后利用甘特图来表示整个空域规划过程。将改进后的遗传-粒子群算法用于算例,并与遗传算法比较,结果表明该算法获得的结果更优且收敛速度更快。     

低空智联网组网与控制理论方法

低空智联网（LAIN）作为一种新兴的智能网络,依托于空天地海基础设施构成数字智能网络体系,是空天地一体化网络的重要组成部分,可支撑实现第六代通信技术无缝泛在互联,推动智能网络服务由地面向低空空域的发展。然而,LAIN正处在技术突破阶段,仍面临以下关键挑战尚未解决,主要包括低空空域安全管控困难、频谱干扰严重、多维资源紧缺等问题。因此,面向LAIN体系架构与安全管控问题,综述了目前低空网络的发展现状,指出其对于产业技术变革的意义;然后,从频谱资源、网络资源以及空域资源管理的角度出发,分析了LAIN相关技术的国内外研究现状;进一步,剖析低空飞行器空地频谱共享、感传算一体化组网覆盖、低空空域智能监管等关键技术,为LAIN和低空空域的管理指明进一步的发展方向;最后,提出了LAIN的应用示范,旨在面向低空空域高效运行与安全的重大需求,为下一代空天地一体化网络的进一步发展提供理论依据和技术。     

MPRF脉冲多普勒雷达二维单元均值类恒虚警模块仿真

阐述了MPRF(中脉冲重频)下二维单元均值类CFAR(恒虚警)的基本原理和它在MPRF多普勒雷达数据处理机中的作用,说明了二维单元均值CFAR的一般流程,并给出了二维单元均值CFAR仿真模块的实现方法,在脉冲多普勒雷达电子对抗仿真系统的大环境下,验证了二维单元均值CFAR仿真模块测试结果的正确性.     

空域网格化方法及其在空管中的应用研究

传统的空域理论方法面临交通密度快速增长、空域管制对象复杂多类异构等一系列问题,研究空域数字化建模,发展出新的空中交通四维时空框架,在此基础上开展全新的空域与空中交通流量协同管理是当前的迫切需要。本文聚焦包括平面格网模型以及空域空间格网模型的空域网格化方法的总结,并对空域网格化方法的空管应用研究进行综述,在此基础上,对空域网格化及数字化研究重点及发展趋势进行综合分析。本文研究能够为空域网格化及数字化理论与应用的可持续发展提供科学指引。     

基于回归分析的低空空域资源可用性评价

低空空域资源一直是通用航空产业发展的瓶颈,是制约我国通用航空发展的关键因素之一。基于理论研究和实地调研,构建低空空域资源可用性评价指标体系,运用多元线性回归法对影响低空资源的军/民航各项因素进行系统分析,确定各级指标权重;以江苏省为例,深入分析该省低空可利用资源的现状,总结各地区实际情况和大量数据,通过多元线性回归结果显示各项因素对该地区的影响值;最后得出低空空域资源可用性结果。结果表明:采用回归分析方法分析低空空域资源,所得结果是科学且有效的,可为我国低空空域资源合理利用以及通用航空活动空域申报提供参考。     

Analysis of some modified ordered statistic CFAR: OSGO and OSSOCFAR

It is necessary for automatic detection radars to be adaptive to variations in background clutter in order to maintain a constant false alarm rate (CFAR). A CFAR based on an ordered statistic technique (OS CFAR) has some advantages over the cell-averaging technique (CA CFAR), especially in clutter edges or multiple target environments; unfortunately the large processing time required by this technique limits its use. The authors present two new OS CFARs that require only ahlf the processing time. One is an ordered statistic greatest of CFAR (OSGO), while the other is an ordered statistic smallest of CFAR (OSSO). The OSGO CFAR has the advantages of the OS CFAR with only a negligible increment to the CFAR loss     

Analysis of CFAR processors in homogeneous background

Five different constant false alarm rate (CFAR) radar processing schemes are considered and their performances analyzed in homogeneous and nonhomogeneous backgrounds, the latter specifically being the multiple target environment and regions of clutter transitions. The average detection threshold for each of the CFAR schemes was computed to measure and compare the detection performance in homogeneous noise background. The exponential noise model was used for clear and clutter backgrounds to get closed-form expressions. The processor types compared are: the cell-averaging CFAR, the `greatest of' CFAR, the `smallest of' CFAR, the ordered-statistics CFAR, and a modified ordered-statistics processor called the trimmed-mean CFAR     

常用雷达视频信号处理算法在FPGA上的实现

采用FPGA技术对雷达原始的视频信号进行求模、视频积累与恒虚警检测处理,克服了DSP处理速度有限、实时性差和ASIC器件灵活性差的问题。详细介绍了求模、视频积累以及恒虚警算法在FPGA芯片上实现的原理和过程,并结合仿真结果说明了利用FPGA进行雷达视频信号处理的优势。     

Adaptive A/D converter for improved DS/SS jam resistance

The use of polar nonlinear processing to reject a constant carrier envelope (and possibly wideband) jammer of a DS/SS spread spectrum link is explored, with the additional objective of ensuring constant false alarm rate (CFAR) in the reception of the associated DS/SS synchronization preamble. It is shown that the constant carrier envelope jammer can be rejected very effectively, with CFAR, whether it is transmitted continuously or pulsed at some maximally damaging duty cycle     

Importance sampling for characterizing STAP detectors

This paper describes the development of adaptive importance sampling (IS) techniques for estimating false alarm probabilities of detectors that use space-time adaptive processing (STAP) algorithms. Fast simulation using IS methods has been notably successful in the study of conventional constant false alarm rate (CFAR) radar detectors, and in several other applications. The principal objectives here are to examine the viability of using these methods for STAP detectors, develop them into powerful analysis and design algorithms and, in the long term, use them for synthesizing novel detection structures. The adaptive matched filter (AMF) detector has been analyzed successfully using fast simulation. Of two biasing methods considered, one is implemented and shown to yield good results. The important problem of detector threshold determination is also addressed, with matching outcome. As an illustration of the power of these methods, two variants of the square-law AMF detector that are thought to be robust under heterogeneous clutter conditions have also been successfully investigated. These are the envelope-law and geometric-mean STAP detectors. Their CFAR property is established and performance evaluated. It turns out the variants have detection performances better than those of the AMF detector for training data contaminated by interferers. In summary, the work reported here paves the way for development of advanced estimation techniques that can facilitate design of powerful and robust detection algorithms     

Recursive Methods for Computing Detection Probabilities

Recursive methods are drived for computing detection probabilities for general fluctuating targets in Gaussian noise. For the generalized chi-square family of fluctuating targets, very simple and convenient recursive algrithms result. The methods are also extended to cell-averaging CFAR. Although the detection probability is expressed iw: terms of an infinite series, a convenient expression is derived for the resulting error when the series is truncated. Cell-averaging CFAR results are computed for nonfluctuating, Swering case I, and Swerling case II fluctuating targets.     

Distributed CFAR detection in homogeneous and nonhomogeneousbackgrounds

The performance of distributed constant false alarm rate (CFAR) detection with data fusion both in homogeneous and nonhomogeneous Gaussian backgrounds is analyzed. The ordered statistics (OS) CFAR detectors are employed as local detectors. With a Swerling type I target model, in the homogeneous background, the global probability of detection for a given fixed global probability of false alarm is maximized by optimizing both the threshold multipliers and the order numbers of the local OS-CFAR detectors. In the nonhomogeneous background with multiple targets or clutter edges, the performance of the detection system is analyzed and its performance is compared with the performance of the distributed cell-averaging (CA) CFAR detection system     

Biparametric linear estimation for CFAR against Weibull clutter

The authors deal with constant false alarm rate (CFAR) procedures against nonstationary clutter, modeled as a Weibull distributed process whose scale parameter α and shape parameter β are both variable. It is shown that conventional CFAR procedures, which compensate only for α, degrade intolerably as β deviates from β=2, namely, as the Rayleigh distributional assumption is violated. A biparametric CFAR procedure is shown to be suited to such situations. The authors introduce a logarithmic transformation to reduce the Weibull probability density function (pdf) to a Gumbel pdf, i.e., to the location-scale type, and then exploit the best linear unbiased estimation (BLUE) of location-scale parameters to adjust the detection threshold. True CFAR is thus achieved when the clutter is locally homogeneous. Resilience against local inhomogeneities can also be conferred since BLUE lends itself to censoring. Through a performance analysis, the influence of various system and distributional parameters is elicited     

中等PRF机载PD雷达频域恒虚警处理性能计算

 一、引言 在中等脉冲重复频率（PRF）工作时,MTI-FFT-CFAR是PD或MTD雷达信号处理器的一种典型结构。Lawrence和Moore对MTD雷达在地杂波和气象杂波背景中的检测性能的计算中,假设了邻近距离单元中的杂波样本是独立同分布的（iid）。而在MTI-FFT-频域单元平均CFAR处理器中,频域单元的杂波样本明显偏离iid假设。门     

CFAR detection and estimation for STAP radar

The algorithm presented here provides both a constant false-alarm rate (CFAR) detection and a maximum likelihood (ML) Doppler-bearing estimator of a target in a background of unknown Gaussian noise. A target is detected, and its parameters estimated within each range gate by evaluating a statistical test for each Doppler-angle cell and by selecting the cell with maximum output and finally comparing it with a threshold. Its CFAR performance is analyzed by the use of the sample matrix inversion (SMI) method and is evaluated in the cases of a fully adaptive space-time adaptive processing (STAP) and two partially adaptive STAPs. The performances of these criteria show that the probability of detection is a function only of the sample size K used to estimate the covariance matrix and a generalized signal-to-noise ratio. The choice of the number K is a tradeoff between performance and computational complexity. The performance curves demonstrate that the finer the resolution is, the poorer the detection capability. That means that one can trade off the accuracy of ML estimation with the performance of the CFAR detection criterion