一种机载前视阵雷达近程杂波谱补偿方法

机载前视阵雷达近程杂波谱在方位-多普勒域随距离变化剧烈,功率谱不重合并严重展宽,直接进行空时自适应处理（STAP）不能在待检测单元形成窄而深的凹口,致使地面动目标检测（GMTI）性能下降。文中提出一种导向矢量矩阵最小二乘拟合方法,该方法利用训练单元和近程待检测单元导向矢量拟合矩阵对杂波数据进行变换,使训练单元的杂波子空间逼近于待检测距离单元,从而实现了机载前视阵雷达近程杂波谱重合。仿真实验表明该方法能够对近程杂波非均匀性进行有效补偿,减轻杂波谱随距离变化对空时自适应处理性能的影响。     

MIMO Radar Space–Time Adaptive Processing Using Prolate Spheroidal Wave Functions

In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on space-time adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity.     

基于自适应分区的非平稳杂波抑制方法

对于非正侧视阵机载雷达,杂波在近程表现出严重的非平稳性,在距离模糊情况下近程微弱目标和近程非平稳强杂波混叠,导致传统空时自适应处理(Space-Time Adaptive Processing, STAP)方法的运动目标检测性能严重下降。为了解决该问题,本文提出了一种基于自适应分区和正交投影的机载雷达非平稳杂波抑制方法。首先,基于回波数据在距离-多普勒域将机载雷达回波自适应划分为非平稳杂波区、平稳杂波区和清晰区,然后在非平稳杂波区采取俯仰维正交投影级联STAP处理,在平稳杂波区采取传统STAP处理,在清晰区采取传统PD处理。该方法能够显著提升机载雷达在全距离和全速度域的目标探测性能。仿真实验验证了所提方法的有效性。     

一种简便的MIMO阵列雷达CFAR检测器

本文提出了一种适用于MIMO阵列雷达的简便的CFAR检测器,它利用了MIMO阵列雷达观测空间维数高的特点,通过直接滤除杂波干扰子空间的方式抑制杂波和干扰。该检测器的简便性在于杂波子空间可以离线估计与存储,而干扰子空间的估计也只需在低维空间上进行,而其原因是在估计杂波和干扰子空间时没有利用距离参考单元观测样本,而是利用了已知的系统参数、杂波子空间结构以及干扰协方差矩阵的模块对角性质。仿真结果表明,在杂波的理想模型条件下,选择适当的估计方法可以获得较高的杂波子空间估计精度,由此得到的CFAR检测器的性能也非常接近于已知杂波干扰子空间条件下的检测性能。      

稳健的局域STAP处理器

针对现有局域空时自适应(STAP)处理器的不足,提出了一种对杂波复杂度和干扰环境变化更加稳健的局域STAP处理器—FDSP (Flexible DOF STAP Processor).该处理器根据外部干扰环境的变化情况,依据准则自适应地调整系统自由度.它不仅降低了非主杂波区杂波抑制的计算量,并且可以有效抑制有源干扰.给出了该处理器的实现方法和权值递推求解的快速算法.利用实测数据证明了该处理器的有效性.     

Space-time adaptive processing for manoeuvring airborne radar

Space-time adaptive processing (STAP) techniques provide simultaneous rejection of jamming and clutter in airborne radar. The greatest benefits over conventional MTI (moving target indication) approaches are in terms of a capability to detect slow-moving targets which possess the same Doppler frequency as mainlobe clutter returns. This paper examines the effects of platform manoeuvre on STAP clutter and jamming rejection performance for a forward-facing array (i.e. where the array is orientated transversally to the direction of travel). It is shown that STAP slow-target detection performance is not sensitive to the radar platform orientation. It is also demonstrated that, under conditions of manoeuvre, STAP can provide better jammer rejection performance than architectures which cascade conventional clutter filtering and spatial adaptive beamforming     

Joint space-time interpolation for distorted linear and bistatic array geometries

This paper presents a new joint space-time interpolation technique (STINT) to improve the small sample support performance of space-time adaptive processing (STAP) with distorted linear monostatic arrays and linear bistatic array configurations. Brennan's rule for the space-time clutter covariance matrix rank is extended to monostatic linear arrays with arbitrary intersensor spacing, distorted linear arrays and bistatic geometries. It is shown that both distortion in the array geometry and bistatic operation increase the clutter rank and cause the space-time clutter covariance matrix to become range dependent. This results in lower output signal-to-interference-plus-noise ratio (SINR) for the same number of adaptive degrees of freedom and reduced available sample support. This motivates the development of the STINT technique aimed at compensating for the clutter rank inflation, while also making the clutter statistics appear more stationary across range. More specifically, a linear transformation is designed that maps the received clutter across space and time to that which would be received by a "virtual" monostatic side-looking ULA. By mapping the data to form a reduced rank clutter covariance matrix, fewer snapshots are needed for a statistically stable matrix inversion as required in STAP, thereby improving the short observation time performance. Simulation results for a typical airborne radar scenario indicate up to 10-dB SINR improvement can be obtained using STINT with limited sample support.     

一种级联结构的空时二维信号处理算法

在研究R．Klemm博士的级联抑制噪声干扰和杂波算法以及旁瓣对消算法的基础上,针对噪声干扰和地杂波同时存在的情况,提出了一种采用旁瓣对消结构级联抑制干扰和杂波的算法。该算法首先利用相控阵天线合成一个辅助天线和多个主天线,多个主天线共用一个辅助天线,针对每一对主辅天线,采用旁瓣对消算法在空域抑制噪声干扰;然后对多个主天线的输出结合时域进行STAP（空时二维信号处理）抑制地杂波。并且证明了该级联结构等效于一种同时抑制干扰和杂波的结构。计算机仿真结果及性能分析验证了该算法能有效地抑制干扰和杂波,且与其他级联结构相比,所提出的结构更利于工程实现。     

Fast reduced-rank STAP algorithm based on Gram–Schmidt orthogonalisation for airborne radar

In the reduced-rank space-time adaptive processing (STAP) methods, especially the principal component (PC) analysis STAP method, a set of dominant eigenvectors must be obtained by singular value decomposition of the space-time covariance matrix. Therefore, it is very difficult to be applied in practical system due to the intense computational complexity. In order to reduce the computational burden, a fast reduced-rank STAP algorithm based on Gram–Schmidt (GS) orthogonalisation is proposed in this article. In the proposed GS-PC STAP method, the clutter subspace is reconstructed by the GS orthogonalisation of training samples. Then, the STAP adaptive weight vector is calculated by orthogonally projecting the quiescent weight vector into clutter subspace, which can hold fast convergence measure of effectiveness (MOE) and require less computational complexity by compared with the conventional PC method. Based on the simulated data and multichannel airborne radar measurements data, the corresponding convergence MOE and the clutter suppression performances are verified in the article.     

一种稳健的共形阵机载雷达杂波抑制方法

针对共形阵机载雷达的杂波抑制问题,提出了一种稳健的空时自适应处理(STAP)方法.该方法首先将待检测距离单元中所含的目标信息剔除,然后利用自回归(AR)模型来描述该单元杂波特性,最后通过求取AR模型系数得到待检测距离单元中杂波正交子空间来对消杂波.该方法不受雷达工作模式影响,适用于任意形状的阵列天线,且不存在干扰目标问...     

稳健的机载预警雷达多通道海杂波抑制方法研究

在机载预警雷达对海洋背景运动目标的探测过程中,雷达平台的高速运动状态使得海杂波多普勒谱发生严重展宽现象,影响目标的检测性能。针对此问题,空-时自适应处理是一种有效的杂波抑制技术,该技术利用杂波的空-时2维耦合特性进行杂波抑制。但相对于陆地杂波而言,海杂波的内部复杂运动特性使得杂波空-时谱发生展宽现象,导致杂波多普勒频率与空间锥角不再保持一一对应关系,从而影响杂波抑制效果。针对海杂波的运动特性,该文提出一种稳健的基于子空间投影的杂波抑制处理算法,所提算法通过滤波凹口自适应展宽技术和先滑窗滤波后自适应处理技术来提高杂波抑制的稳健性。最后通过仿真的海杂波数据和实测海杂波数据验证了所提算法的有效性。     

任意线阵的直接数据域空时自适应处理方法

研究机载预警雷达在前视任意线阵构形下的地杂波和干扰抑制问题.在采用迭代法获得单个距离门回波数据的空时二维谱基础上,结合图像特征分析提取杂波和干扰分布特性曲线,通过将待检测单元数据向重构的杂波和干扰子空间正交投影检测动目标.能避免直接数据域方法存在的空时孔径损失和克服传统统计空时自适应处理(STAP)方法在非平稳环境性能...     

基于SA-MUSIC理论的联合稀疏恢复STAP算法

基于子空间扩展多重信号分类(SA-MUSIC)理论对杂波空时二维谱进行联合稀疏恢复,实现小样本情况下空时自适应处理(STAP)性能的显著提升.首先,提出空时导向矢量相关性模型,利用该模型分析杂波在空时二维平面上的稀疏本质,解释用部分空时导向矢量近似整个杂波子空间的合理性.其次,提出基于SA-MUSIC理论的联合稀疏恢复STAP算法(SA-MUSIC-STAP),该算法仅需极少训练样本便可实现对杂波协方差矩阵的准确估计,并实现有效的杂波抑制.仿真实验验证了SA-MUSIC-STAP算法的有效性.     

一种基于空时内插的双基地机载雷达杂波抑制方法

杂波非均匀是双基地机载雷达杂波抑制面临的一大难题,直接制约着常规STAP方法的杂波抑制性能。该文提出了一种改进的联合空时内插方法(ImSTINT)。相对于传统的联合空时内插方法,ImSTINT方法映射变换后的子空间杂波自由度更小,因此能够彻底消除非均匀杂波对双基地机载雷达杂波抑制性能的影响。此外,该方法具有更快的收敛速度和更好的主杂波抑制性能。     

基于环境动态感知的空时自适应处理

在非均匀环境中,缺乏独立同分布的训练样本会使空时自适应处理(Space-Time Adaptive Processing, STAP)算法性能严重下降。针对这个问题,该文提出一种基于环境动态感知的空时自适应处理方法。该方法首先通过发射一组正交信号感知观测场景获取杂波信息;然后利用杂波信息结合平台参数及系统参数预测未来一段时间内杂波的协方差矩阵;最后将预测的协方差矩阵与样本协方差矩阵进行组合以构造空时滤波器。仿真结果表明,与传统的知识辅助类STAP算法相比,该方法在缺乏准确先验知识的情况下依然可以有效地抑制非均匀环境中的杂波。     

基于传播算子的机载前视阵雷达杂波谱补偿方法

 针对机载前视阵中杂波多普勒频率随距离发生变化,导致杂波谱在距离上呈现严重的非平稳性,因此根据杂波模型提出了一种新的杂波谱补偿方法,该方法首先对接收数据进行分块处理来构造传播算子以获得杂波子空间,再将训练单元的杂波子空间向待检单元的杂波子空间进行投影得到一个变换矩阵,然后用这个变换矩阵对数据进行处理,使杂波谱得到补偿,最后利用空时自适应算法对补偿后的数据进行杂波抑制.仿真结果表明此方法有效地抑制杂波的非平稳性,提高了雷达抑制杂波的性能.     

基于空时自适应处理的低空风切变风速估计方法

针对机载气象雷达在探测低空风切变时,有用信号会淹没在强杂波背景中的问题,该文提出一种基于空时自适应处理(STAP)的低空风切变风速估计方法。该方法首先利用空时插值原理校正机载前视阵地杂波的距离依赖性,获得多个独立同分布(IID)样本后估计地杂波协方差矩阵,然后构造适用于分布式低空风切变目标的空时自适应处理器,在自适应抑制地杂波的同时积累低空风切变信号,最终实现风场速度的精确估计。仿真结果表明,在高杂噪比、低信噪比的情况下,该方法可有效地自适应抑制地杂波并精确地估计风场速度。     

基于原子范数的无网格稀疏恢复非正侧视阵空时自适应处理算法

基于杂波谱稀疏恢复的空时自适应处理(STAP)方法可以显著降低对杂波样本数的要求,十分适合缺少样本情况下的机载雷达杂波抑制。然而,现有稀疏恢复STAP方法利用离散化空时导向矢量字典进行重构,在非正侧视阵情况下,由于杂波脊不在字典网格点上,字典失配问题严重影响杂波抑制性能。针对上述问题,该文提出了一种基于原子范数的无网格稀疏恢复空时自适应处理方法(ANM-STAP),利用低秩矩阵恢复理论实现连续空时平面的稀疏恢复,克服了稀疏恢复中的字典失配问题,获得了非正侧视阵情况下的高分辨率杂波空时谱,有效提高了STAP杂波抑制性能。Monte Carlo实验证明,该文方法STAP处理性能在非正侧视阵情况下优于已有字典离散化处理的稀疏恢复STAP方法。     

基于卷积神经网络的天基预警雷达杂波抑制方法

利用天基预警雷达实现动目标指示具有重要的军事应用价值。对于天基预警雷达,其平台高速运动及受地球自转影响导致杂波复杂非平稳性,更大的波束照射区域带来更严重的杂波非均匀性,从而导致适用于机载预警雷达的传统空时自适应处理(STAP)方法无法直接应用。针对上述问题,该文分析了天基预警雷达杂波分布特性,并构建了基于卷积神经网络(CNN)超分辨谱估计的STAP处理框架。首先,利用雷达系统和卫星轨道参数,仿真随机生成不同纬度、距离门、阵元误差、杂波起伏和地貌散射系数的回波数据集;然后,设计并调优了含5个权重层的二维CNN,实现由小样本所估低分辨杂波谱到高分辨谱的非线性映射;最后,基于高分辨空时谱构造空时滤波器实现杂波抑制和目标检测。仿真实验验证所提方法在小样本条件下可实现次最优杂波抑制性能,同时所需在线运算量远低于现有稀疏超分辨类方法,因此适用于天基预警雷达实际应用。      

Clutter suppression for airborne phased radar with conformal arrays by least squares estimation

The radar clutter statistics for airborne conformal arrays varies by range, i.e., the clutter distributions are nonstationary, which causes performance degradation for the conventional space-time adaptive processing (STAP), which estimates the clutter covariance matrix (CCM) from data at adjacent range cells. In this paper, a novel clutter suppression method for airborne phased radar with conformal arrays is proposed that takes a form of corrected sample matrix inversion (SMI) through the CCM estimated by the least squares (LS) estimation. The estimated CCM can provide partial information about the real CCM in the novel method, which results in improved detection performance for targets in conformal array applications. Simulation results relative to several typical conformal arrays verify the effectivity of the presented method.