曲线轨迹下前视多通道SAR解模糊成像算法

前视合成孔径雷达（SAR）存在左右模糊问题，在直线航迹下，目标关于航线对称模糊，模糊关系不随航线变化，可利用波束形成进行全孔径解模糊处理；但在曲线航迹下，目标的模糊关系具有空变性，不能全孔径解模糊成像。针对这一问题，分析了曲线航迹下目标模糊关系的空变特性，提出一种前视多通道SAR快速后向投影子孔径处理解模糊成像算法。首先进行子孔径划分，在每个子孔径内目标的模糊关系近似不变，每个子孔径分别成像，然后通过波束形成对每幅子图像进行解模糊处理，最后融合子图像得到最终的成像结果。算法有效解决了曲线轨迹下目标模糊关系的空变性对解模糊的影响，仿真实验验证了算法的有效性。     

基于杂波子空间估计的MIMO雷达降维STAP研究

 多输入多输出(MIMO)雷达是近年来出现的一种新体制雷达,针对MIMO体制的机载雷达开展空时自适应处理（STAP）技术研究是值得进一步努力的方向。本文研究了机载MIMO雷达STAP技术的降维算法,通过对STAP技术杂波抑制原理进行分析,推导并得到一种基于杂波子空间的降维算法。结合扁长椭球波函数（PSWF）的特点,提出了一种基于杂波子空间估计的降维算法,并与若干降维算法的杂波抑制性能进行比较。结果表明,当存在阵元幅相误差时,该算法在保持杂波抑制性能的同时能够有效地降低STAP算法的运算量。     

基于3DT的空时自适应单脉冲参数估计算法

空时自适应处理(STAP)是机载预警雷达抑制杂波和干扰的一项关键技术,而多普勒三通道联合自适应处理(3DT)是适合工程实现的降维(RD)STAP方法。STAP目标检测后还需进一步估计目标的角度参数,因此将自适应单脉冲(AM)技术引入3DT,提出了一种高精度联合估计目标速度与方位空间角的空时自适应单脉冲算法。理论分析与仿真实验结果表明,当目标多普勒频率偏离检测多普勒单元中心频率时,该算法能同时减少目标多普勒跨越损失和空时导引矢量失配损失,进而提高输出信杂噪比(SCNR),改善目标测角精度。     

圆台阵列杂波模型及空时二维自适应处理

针对圆台共形阵列,建立了空时二维自适应处理(STAP)的杂波模型,给出了圆台阵列杂波抑制最优权值的计算方法。在此基础之上,为了实现可应用到实际环境中的自适应处理方法,进一步讨论了将局部联合域(JDL)降维算法推广至圆台阵列中的问题。得出了圆台阵列JDL算法降维变换矩阵的表达形式,研究了参考波束的数目选取、波束指向等因素对降维损失的影响。理论分析以及仿真结果表明,通过合理选择通道数、波束方位向指向间隔等参数,该算法能够减少自适应波束形成的计算量,而且可以用较少的训练样本获得较好的处理性能。     

相控阵测控系统的特点和主要技术问题

在比较相控阵测控系统和相控阵雷达的基础上，归纳出相控阵测控系统的特点，根据研制相控阵多目标测控系统和TDRSS-SMA的体会，概述了它的测轨精度、波束形成、分时多波束和同时多波束、自适应时-空二维滤波、边扫描边跟踪和多目标跟踪、共形相控阵和宽角扫描以及降低造价等问题。     

毫米波波束编码技术在无人机智能集群中的应用

毫米波波束编码技术由于其速率高、抗干扰能力强的优点被认为是无人机智能集群通信网络的重要解决方案。但在无人机智能集群的通信场景中,多种原因造成的机体不稳定抖动会使通信波束产生小角度偏转,引起通信质量的下降,从而影响无人机集群的控制与决策。针对这一问题,提出一种面向无人机集群通信的自适应波束设计方法。首先,根据传感器回传的机体波束抖动情况建立等效信道模型,随后利用量化的信道模型参数建立目标函数并获得理想的波束编码向量,在此基础上利用几何贪婪算法对其进行分解。仿真结果表明,提出的集群波束编码方法能够有效提高均值通信速率,同时相较于其他的系数分解算法,有效降低了计算复杂度。     

一种基于协方差矩阵加权的阵元误差补偿STAP处理

机载多通道阵列雷达天线在工程实践中不可避免地存在各类阵元误差,所产生的通道失配问题会对空时二维自适应处理的性能造成大的影响。对存在阵元误差时的阵列信号模型进行了分析,提出了一种基于协方差矩阵加权（CMT）的阵元误差补偿空时自适应处理（STAP）方法,在工程应用中该加权矩阵可通过地面天线定标及校飞过程确定,通过对总干扰协方差矩阵估计的加权预处理,可将实际阵元误差对STAP性能的影响控制在测量误差的影响范围,最后通过仿真验证了算法的有效性。     

基于稀疏阵列和码分信号的机载预警雷达STAP研究

基于稀疏阵列和码分正交信号,研究了机载雷达的空时自适应处理（STAP）技术,用于空中预警背景下的地面杂波抑制和运动目标探测。提出了稀疏阵列码分多相位中心孔径综合方法,采用正交编码信号实现多发多收,使综合后不同编码信号的相位中心在数量和分布情况上和满阵天线的相同,从而避免了稀疏阵列天线旁瓣较高的问题;在孔径综合的基础上,利用空时自适应处理方法完成杂波抑制,实现运动目标检测。仿真结果表明了本文方法的有效性。     

一种机载雷达两级降维空时二维自适应处理方法

 提出了一种机载相控阵雷达杂波抑制的两级降维空时自适应处理(STAP)方法,即：先根据杂波分布先验信息进行空时局域化(JDL)降维处理,然后对局域化输出进行多级维纳滤波(MWF),实现二次降维。该方法综合了固定结构和自适应结构降维技术的优点,将JDL处理引入到MWF中,从而有效降低MWF的杂波自由度。计算机仿真和理论分析表明本文方法比JDL自适应处理方法和全空时MWF方法具有更小的运算量,对阵元随机幅相误差具有很好的容差能力,是一种稳健的两级降维自适应处理方法。最后,基于仿真和实测数据的实验验证了算法的有效性。     

基于降维STAP 的自适应单脉冲测角算法

将空域广义单脉冲测角算法扩展至空时二维,提出基于降维STAP的自适应单脉冲测角算法,通过实时自适应修正鉴角曲线来降低角误差,实现杂波环境下的目标角误差估计,并且计算复杂度较低,从而保证机载平台在强杂波环境下对目标的稳定跟踪.仿真结果验证了该方法的有效性与性能优势.     

基于稀疏贝叶斯学习的字典失配杂波空时谱估计方法

杂波谱稀疏恢复空时自适应处理（STAP）是一种有效减少杂波样本数需求的机载雷达杂波抑制方法。然而,空时平面被离散地划分为若干个网格点来构建空时导向矢量字典,当字典在失配时,杂波脊不能准确落在预先离散化的网格点上,稀疏恢复STAP性能严重下降。提出了一种基于稀疏贝叶斯学习的字典失配杂波空时谱估计方法,首先利用二维泰勒级数建立空时动态字典模型,然后将字典失配误差作为待估超参数构建贝叶斯稀疏恢复模型,并利用失配误差估计值对空时导向矢量字典进行修正,最后利用修正后的空时导向矢量字典重构杂波协方差矩阵,进而计算杂波空时谱。实验证明,该方法能够有效提高字典失配情况下的杂波谱稀疏恢复精度,杂波抑制性能优于已有字典预先离散化的稀疏贝叶斯学习STAP方法。     

A sampling-based approach to wideband interference cancellation

Classical adaptive array schemes which use only complex spatial weights are inherently narrowband and consequently perform poorly when attempting to suppress wideband interference. The common solution to this problem is the use of tapped delay line filters in each spatial channel to facilitate space-time adaptive processing (STAP). The higher performance provided by the STAP architecture comes at the cost of a considerable increase in complexity. This paper presents a simpler technique based on programmable time adjustable sampling (TAS) that provides a limited number of wideband degrees of freedom. Two TAS methods are introduced: TAS-sidelobe canceler (TAS-SLC) is based on the sidelobe canceler, while TAS-minimum variance beamformer (TAS-MVB) is derived from the minimum variance beamformer. TAS is implemented by adjusting the sampling instant at selected array channels. TAS-SLC consists of controlling the sampling in the main channel of the sidelobe canceler With TAS-MVB array complex weights are substituted with TAS time delays. The performance of TAS methods with wideband interference is compared to the conventional sidelobe canceler and minimum variance beamformers. It is shown that TAS-SLC provides better performance than the sidelobe canceler, while TAS-MVB outperforms the minimum variance beamformer     

Target Detection and Parameter Estimation for MIMO Radar Systems

We investigate several target detection and parameter estimation techniques for a multiple-input multiple-output (MIMO) radar system. By transmitting independent waveforms via different antennas, the echoes due to targets at different locations are linearly independent of each other, which allows the direct application of many data-dependent beamforming techniques to achieve high resolution and excellent interference rejection capability. In the absence of array steering vector errors, we discuss the application of several existing data-dependent beamforming algorithms including Capon, APES (amplitude and phase estimation) and CAPES (combined Capon and APES), and then propose an alternative estimation procedure, referred to as the combined Capon and approximate maximum likelihood (CAML) method. Via several numerical examples, we show that the proposed CAML method can provide excellent estimation accuracy of both target locations and target amplitudes. In the presence of array steering vector errors, we apply the robust Capon beamformer (RCB) and doubly constrained robust Capon beamformer (DCRCB) approaches to the MIMO radar system to achieve accurate parameter estimation and superior interference and jamming suppression performance.     

Reduced-rank STAP for high PRF radar

Due to the range ambiguity of high pulse-repetition frequency (HPRF) radars, echoes from far-range fold over near-range returns. This effect may cause low Doppler targets to compete with near-range strong clutter. Another consequence of the range ambiguity is that the sample support for estimating the array covariance matrix is reduced, leading to degraded performance. It is shown that space-time adaptive processing (STAP) techniques are required to reject the clutter in HPRF radar. Four STAP methods are studied in the context of the HPRF radar problem: low rank approximation sample matrix inversion (SMI), diagonally loaded SMI, eigencanceler, and element-space post-Doppler. These three methods are evaluated in typical HPRF radar scenarios and for various training conditions, including when the target is present in the training data     

Stap using knowledge-aided covariance estimation and the fracta algorithm

In the airborne space-time adaptive processing (STAP) setting, a priori information via knowledge-aided covariance estimation (KACE) is employed in order to reduce the required sample support for application to heterogeneous clutter scenarios. The enhanced FRACTA (FRACTA.E) algorithm with KACE as well as Doppler-sensitive adaptive coherence estimation (DS-ACE) is applied to the KASSPER I & II data sets where it is shown via simulation that near-clairvoyant detection performance is maintained with as little as 1/3 of the normally required number of training data samples. The KASSPER I & II data sets are simulated high-fidelity heterogeneous clutter scenarios which possess several groups of dense targets. KACE provides a priori information about the clutter covariance matrix by exploiting approximately known operating parameters about the radar platform such as pulse repetition frequency (PRF), crab angle, and platform velocity. In addition, the DS-ACE detector is presented which provides greater robustness for low sample support by mitigating false alarms from undernulled clutter near the clutter ridge while maintaining sufficient sensitivity away from the clutter ridge to enable effective target detection performance     

机载MIMO雷达空时自适应杂波对消器

针对机载多输入多输出(MIMO)雷达杂波分布呈现空时耦合特性,提出一种空时自适应杂波对消器.利用机载MIMO雷达的脉冲回波数据,构造杂波对消器的系数矩阵.通过空时自适应杂波对消器的预处理,可以有效地抑制杂波,并通过与常规空时处理算法的级联,最终可以有效提高动目标的检测性能.实现了由传统地基雷达杂波对消器向机载运动平台的推广.仿真结果表明,这种自适应杂波对消器不仅适用于正侧视雷达,对于非正侧视雷达也同样适用.     

鲁棒成形极化敏感阵列波束的方法及极化估计

基于极化敏感阵列,提出了一种鲁棒成形阵列波束的方法。该方法首先将阵列的数据模型进行了重新描述,从而获得了信号波达角(DOA)和极化解耦的模型。借助于该模型并对信号的两个极化方向分别进行鲁棒约束,设计出了一个新的鲁棒空域波束空间成形矩阵,利用该矩阵可以获得信号两个极化分量的鲁棒估计。基于特征值分解的方法,最后给出了估计信号极化参数的方法。分析和数值仿真实验均表明:提出的方法,在对DOA估计误差以及阵列位置误差等造成的阵列失配具有较强鲁棒性的同时,也能有效抑制干扰和噪声,进而提升了极化参数估计的性能。     

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     

基于改进Yolov3 算法的舰船目标检测识别系统

针对复杂战场环境下对海目标检测识别的需求,设计了一种基于改进Yolov3 算法的海面舰船目标实 时检测识别系统。使用微调分类网络、增加训练尺度、聚类目标边框维度、二级特征分类等方法对Yolov3 检 测识别网络模型进行了优化,在提高识别精度的同时有效降低了漏检率和虚警率。实验结果表明,优化后的网 络模型在自建的舰船图像数据库中将检测识别平均准确率提高到了79.3%,对真实海上航拍视频中舰船目标识 别的平均准确率达到了81% 以上。