利用直接数据域的双基机载雷达地面动目标检测方法

双基机载雷达的杂波存在严重的距离依赖性,从距离上获得的样本不能够准确估计杂波加噪声协方差矩阵,从而导致空时自适应处理(STAP)的杂波抑制性能严重下降。针对该问题,提出了利用直接数据域(DDD)的双基机载雷达地面动目标检测方法。因为DDD算法是在单个距离门对目标滤除后进行空时平滑来获得足够多的样本,所以不需要对双基雷达杂波距离依赖性进行补偿。仿真实验验证了该方法的有效性。     

非均匀杂波中的直接变换域STAP算法研究

降维空时自适应处理和降秩空时自适应处理均需要已知杂波协方差矩阵,或者通过参考单元对杂波协方差矩阵进行估计,在非均匀杂波环境中无法获取足够多的有效样本,使得算法性能急剧下降。提出一种直接变换域空时自适应处理算法,不需要对杂波协方差矩阵估计,在角度-多普勒域中的通道之间进行自适应处理,采用最小均方准则求解,能够适用于非均匀杂波环境中目标检测。仿真结果表明,提出的算法相比于直接数据域算法,抑制杂波能力更强,对于目标角度偏差更具稳健性。     

机载双基地雷达STAP分析

空时自适应处理(STAP)是机载雷达信号处理中的一项关键技术,近年来机载双基地雷达STAP的研究成为热点.机载双基地雷达表现出复杂的杂波特性,即严重的杂波距离相关性,导致传统STAP方法杂波抑制性能严重下降.为此,本文首先对现有解决机载双基地雷达STAP面临的杂波距离相关性问题的典型方法的机理和特点进行了分析,然后对各...     

一种全谱域的机载前视阵雷达杂波补偿方法

机载前视阵雷达近程不同距离门杂波的方位-多普勒分布不同,导致杂波具有强烈的距离依赖性,从而使得杂波噪声协方差矩阵估计不准确,空时自适应处理（STAP）的杂波抑制性能严重下降。针对该问题,提出了一种全谱域的杂波距离依赖性补偿方法。以雷达先验参数知识为基础重构各距离门的杂波协方差矩阵,对重构的杂波协方差矩阵向参考距离门进行一定变换,进而实现全谱域的补偿。仿真实验结果证明了该方法的有效性。     

基于特征值分布的自适应MIMO接收方法

多输入多输出(MIMO)系统性能优劣依赖于信道的相关特性,空分复用技术适用于低相关信道,而波束形成技术适用于高相关信道。依据上述信道相关特性,提出一种基于信道特征值分布的自适应MIMO接收方法。该方法以均匀圆形天线阵列结构为基础,能够根据信道相关情况动态选择接收方式:当角度扩展较大时,选用传统MIMO接收方式(称为天线MIMO方式);当角度扩展较小、多径独立时,选用基于智能天线的多波束接收方式(称为波束MIMO方式)。分析角度扩展、多径对信道特征值分布的影响,并给出自适应MIMO接收切换的条件。仿真结果表明,该方法能够适应复杂的无线环境,误码率较低,性能更加稳定可靠。     

机载雷达杂波抑制优化与性能分析

为了有效分析机载雷达杂波抑制特性和提高目标检测性能.建立了天线行子阵和列子阵的天线发射方向图模型,同时采用空时自适应处理(Space Time Adaptive Processing,STAP)的机载雷达杂波分析方法,根据目标的导向矢量和杂波与噪声的协方差矩阵建立了杂波功率谱的数学模型,选取不同的脉冲重复频率仿真分析了杂波的特征值谱和改善因子以及杂波空时谱.仿真结果表明:低脉冲重复频率和中脉冲重复频率使杂波谱扩散;随着脉冲重复频率的减少,杂波的相关度减少;STAP法适合高脉冲重复频率的情形,为抑制雷达杂波提供了依据.     

基于非均匀检测的机载雷达空时自适应处理方法

提出了一种新的非均匀杂波环境下的数据样本选取方法－－关联维数（ＣＤ）非均匀检测器,研究了非均匀杂波环境中改进干扰协方差矩阵的估计和自适应权的形成问题。仿真结果表明,非均匀检测器与空时自适应处理（ＳＴＡＰ）方法相结合,可显著地改进机载雷达的检测性能。     

基于修正STAP的高速空中机动目标检测方法

空时自适应处理(Space-time adaptive processing,STAP)是一种有效的机载雷达运动目标检测方法.空中目标的高速运动会导致其回波产生严重的距离走动和多普勒模糊,并且目标作加速运动时还会导致其多普勒频率随时间变化(即多普勒走动).为了有效检测上述目标,本文提出了一种新方法,该方法在目标距离走动校正之前首先进行杂波抑制,避免了直接利用Keystone变换校正存在多普勒模糊的运动目标距离走动时影响杂波分布特性,进而降低STAP性能的问题;对距离走动校正后的数据进行修正STAP,估计出目标的加速度,并根据估计值对多普勒走动项进行补偿;最后进行常规空时二维波束形成来实现目标能量积累.仿真结果证明了该方法的有效性.     

针对地球自转影响的星载正侧面阵雷达杂波补偿方法

对于星载正侧面阵雷达,地球自转会导致地面杂波的多普勒分布发生展宽,使得杂波协方差矩阵估计不准确,致使空时自适应处理(STAP)的杂波抑制性能下降.针对该问题,本文提出了一种杂波补偿方法.该方法利用星载雷达的轨道参数对地球自转分量进行估计,然后对杂波数据进行多普勒补偿.该方法能够减轻由于地球自转引起的杂波多普勒分布展宽,...     

机载MIMO STAP收发天线联合设计研究

针对机载多输入多输出雷达进行动目标检测时,天线稀疏出现栅瓣导致盲速、天线密布导致冗余及布阵需考虑平台尺寸等问题,对天线结构进行研究.首先基于相位中心等效原理,讨论一定收发阵元数目下如何实现最大自由度和最大孔径;然后根据机载平台结构,设计更合理的收发天线布阵结构;最后结合多相编码进行空时自适应处理,实现动目标检测和杂波抑制.仿真实验表明,与其他天线结构相比,本文方法可获得更好的动目标检测性能.     

A novel approach for wide‐swath and high‐resolution SAR image generation from distributed small spaceborne SAR systems

In this paper we investigate the possibility to produce high‐resolution synthetic aperture radar (SAR) images of wide areas from the data collected by the distributed small spaceborne SAR systems. In particular, we focus on the data processing techniques for overcoming the Doppler ambiguity effects characterizing ground echoes and achieving high‐resolution SAR images of wide areas. A novel approach is proposed, the key idea of which is to retrieve the unambiguous azimuth wide (full) spectrum signal from the received ground echoes by using a space–time adaptive processing (STAP) algorithm to overcome the Doppler aliasing effect induced by the lower pulse repetition frequency (PRF); following this operation, conventional SAR data processing tools can be applied to fully focus the SAR images. The STAP‐based approach has the advantages of simplicity, robustness and high efficiency. We also discuss the array configuration requirements, and give a platform velocity/PRF criterion for array configurations.     

Robust space–time adaptive processing with colored loading using iterative optimization

The performance of space–time adaptive processing (STAP) may degrade dramatically if some undesired mismatches exist in real scenarios, such as array calibration error, distorted antenna shape, direction of arrival (DOA) and Doppler frequency mismatches between the actual and presumed responses to the desired target signal, insufficient training data samples and so on. In this paper, we develop a new approach to STAP that is robust to different variations in real scenarios. This method is based on the iterative optimization for the spatial–temporal separate filter. It is confirmed that this method belongs to the class of colored loading algorithms. The loading factor can be efficiently calculated based on the known level of the uncertainty mismatch sets of spatial temporal steering vectors. Computer simulations demonstrate that the proposed robust two-dimensional (2-D) beamformer with colored loading has attained better performance as compared to the conventional STAP algorithm.     

知识辅助的机载MIMO雷达降秩STAP算法

研究机载平台下的MIMO雷达空时自适应处理技术(STAP),基于广义旁瓣相消结构的多级维纳滤波器,提出了一种利用先验知识约束的MIMO雷达降秩STAP算法。通过利用干扰方向知识以及基于扁长椭球波函数估计的杂波子空间知识,在保证杂波抑制性能的基础上,大大降低了机载MIMO雷达STAP算法的运算量和样本需求量。同时,考虑了知识不匹配情况下,知识辅助对算法收敛性能的影响。仿真结果表明,当知识存在一定误差时,该算法仍能有效地改善STAP算法的收敛性能。     

Adaptive processing for beamforming with coherent interference using uniform circular arrays

The paper deals with the problem of adaptive array beamforming using a uniform circular array (UCA) in the presence of coherent interference. The well-known scheme of spatial smoothing (SS) widely used to tackle the coherent problem in the cases of 1-D uniform linear array (ULA) and uniform rectangular array (URA) cannot work for the UCA case. This is mainly due to the fact that the direction vector of each signal received by a UCA does not possess a Vandermonde structure. In this paper, we present a method based on subarray beamforming in conjunction with an averaging scheme to effectively mitigate the effect due to coherent interference in the UCA case. Theoretical analysis regarding the validity of the proposed method is also presented. Several simulation examples are provided for showing the effectiveness of the proposed method.     

机载雷达级联降维空时自适应杂波抑制方法

提出一种机载雷达杂波抑制的级联降维空时自适应算法,即,先对全空时两维接收数据进行预滤波处理,将杂波局域化,降低杂波自由度;然后对预处理输出的信号的相关矩阵进行子阵划分,求解低维权向量,进一步降低运算量和采样要求。理论分析和实验仿真结果表明,所提算法具有良好的收敛性能和杂波抑制能力,并且对于阵元随机幅相误差和杂波起伏具有很好的容差能力。基于实测数据的实验验证了算法的有效性和稳健性。     

基于模式空间算法的声源二维DOA估计

针对传统高分辨率谱估计法估计远场声源波达方向（direction of arrival, DOA）时计算量大、对相干信号估计失准的问题,本文提出一种改进的基于圆形麦克风阵列和四阶累积量的声源二维DOA估计算法。该算法结合了圆阵定位无死角的优势和矩阵虚拟扩展获得更多声源定位信息的长处。首先利用模式空间变换将均匀圆形阵列（UCA）虚拟化成2K+1个均匀线性阵列（ULA）,并应用空间平滑技术将虚拟线性阵列划分成L个子阵;接着利用四阶累积构造方法提取有效阵元信息并去掉冗余数据,通过重构矩阵得到新的接收数据;最后通过Music-like算法搜索谱峰获得声源信号的方位角和俯仰角。仿真结果表明,在信噪比较低时,相比传统的高分辨谱估计算法,本文算法可实现对远场相干信号的高精度估计;同时本文算法也具有更低的均方根误差性能,且能有效减少运行时间。     

Robust and fast iterative sparse recovery method for space-time adaptive processing

Conventional space-time adaptive processing (STAP) requires large numbers of independent and identically distributed (i.i.d) training samples to ensure the performance of clutter suppression, which is hard to be achieved in practical complex nonhomogeneous environment. In order to improve the performance of clutter suppression with small training sample support, a robust and fast iterative sparse recovery method for STAP is proposed in this paper. In the proposed method, the sparse recovery of clutter spatial-temporal spectrum and the calibration of space-time overcomplete dictionary are achieved iteratively. Firstly, the robust solution of sparse recovery is derived by regularized processing, which can be calculated recursively based on the block Hermitian matrix property, afterwards the mismatch of space-time overcomplete dictionary is calibrated by minimizing the cost function. The proposed method can not only alleviate the effect of noise and dictionary mismatch, but also reduce the computational cost caused by direct matrix inversion. Finally, the proposed method is verified based on the simulated and the actual airborne phased array radar data, which shows that the proposed method is suitable for practical complex nonhomogeneous environment and provides better performance compared with conventional STAP methods.     

一种子阵划分方法及子阵级STAP性能分析

对于幅度锥削的均匀线性阵列,本文首先提出了一种有效的基于遗传算法的子阵划分方法,在遗传算法中利用罚函数法避免子阵结构中出现阵元交叉项。然后对子阵级权值进行归一化,使得各子阵级噪声电平相等。最后通过计算机仿真分析了子阵级STAP处理器的性能。仿真结果表明,该子阵结构的天线方向图无明显栅瓣,其STAP杂波抑制性能接近于阵元级STAP处理性能。     

采用二阶锥规划和压缩感知的方向图综合算法

针对常规自适应方向图综合在求解最优权值向量时需经过复杂的广义矩阵求逆运算,计算过程繁琐,占用存储空间大的缺陷,提出一种采用二阶锥规划与压缩感知理论的改进自适应方向图综合算法。改进的算法将传统算法中的误差性能函数通过数学变换转换成标准二阶锥规划形式快速求解。同时应用压缩感知理论将大规模阵列权值稀疏化处理,从而得到最优天线阵列权值向量并减少训练时间。仿真结果表明,采用二阶锥规划与压缩感知的改进算法在大规模阵列天线方向图综合时求解速度快,运算精度高,且在应用压缩感知后形成的方向图有较低旁瓣,干扰方向零陷深,接近满阵时的波束性能。     

一种有效的直接数据域地面动目标检测方法

直接数据域(Direct data domain, DDD)方法利用子孔径平滑来从单个距离门中获得足够样本,但空域、时域孔径损失严重,使得空时自适应处理的地面动目标检测性能下降严重.针对该问题提出了一种不需要协方差矩阵估计和求逆的DDD方法,该方法将多级维纳滤波器引入到DDD的最优权求解过程中,在低的空时孔径损失下仍然能够获得好的性能.某机载实测雷达数据的实验结果验证了该方法的有效性.