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。     

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.     

CFI中基于动态区域划分的非平稳杂波抑制方法

在超声彩色血流成像(Color Flow Imaging,简称CFI)系统中,为抑制杂波信号对血流速度估计的影响,提出了一种基于动态区域划分的非平稳杂波抑制方法。该方法首先根据回波信号的能量特性提出动态区域划分法,将回波信号动态地分割为静态组织区、杂波区和血流区三部分;然后根据信号的时域特性,结合非平稳杂波抑制法和多项式回归法进行非平稳杂波抑制。仿真结果表明,该算法滤波后的杂波血流比约为6.120 dB,能较好地抑制非平稳杂波信号,估计出的血流速度剖面完整,成像质量较高;该算法的运行时间约为0.3414 s,执行速度为非平稳杂波抑制法的3倍,具有较高的实时性。     

局域化联合杂波抑制算法研究

传统的局域化联合算法使用傅里叶变换从脉冲数变换到多普勒域,由于脉冲数的限制,导致了变换后的傅里叶系数采样出现偏差,导致对某些目标速度不易检测,使得检测性能降低。通过改变传统局域化联合算法中的分块模式,把一个多普勒通道划分在两个以上的局域块中,解决傅里叶变换系数采样不准的问题,从而能够改善检测性能,对于某些目标速度,改进的JDL算法可以在信干噪比下降3dB左右时,依然达到和传统JDL算法相同的检测概率。     

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,...     

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。     

非均匀环境下机载雷达STAP方法

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,但其应用的实际杂波环境通常是非均匀的.本文对非均匀环境下各STAP方法进行了科学分类,并详细分析了各方法的内在机理及相互间的内在联系,同时总结评述了各方法的优缺点,最后指出了机载雷达非均匀STAP方法中有待于进一步研究和完善的相关问题.     

非正侧视机载雷达一种改进的多普勒频移算法

非正侧视条件下,机载相控阵雷达的杂波随距离变化而不断改变,各距离单元的回波不再是独立同分布(ⅡD)的数据,由于没有足够多的均匀样本来估计协方差矩阵,使得STAP算法的性能急剧下降。研究非正侧视阵列机载雷达的杂波抑制问题,提出一种改进的多普勒补偿方法以改善非正侧视阵列杂波的非均匀程度。该方法先采用DW处理,使得各距离的杂波谱在主波束方向重合,然后在各多普勒通道内使得待补偿单元的空间角频率与待检测单元的空间角频率相同,从而提高杂波的相似程度。仿真结果表明,所提出的方法杂波抑制性能明显好于DW法和ADC法,但运算量与原有算法大体相当,具有工程应用价值。     

非正侧视机载雷达一种改进的多普勒频移算法

非正侧机载雷达的杂波分布随距离变化而变化,各距离单元的杂波分布不再满足独立同分布条件,导致统计型STAP处理器性能下降。多普勒频移（Doppler Warping,DW）算法沿主波束方向对杂波非均匀进行了补偿,但在其余方向上杂波非均匀依然存在,因而性能较差。提出了一种改进的非正侧视机载雷达杂波抑制算法——修正的多普勒频移法（Modified Doppler Warping,MDW）,先通过多普勒频移法使各距离单元的杂波谱在主波束方向重合,再沿多个多普勒通道使参考单元和检测单元的杂波谱保持一致,进一步消除非正侧视机载雷达的杂波非均匀程度。仿真结果表明,与原有方法相比,该方法的杂波抑制性能有明显提高,且运算量增加不多,是一种具有工程应用价值的方法。     

Nonstationary clutter suppression for airborne conformal array radar

The essence of nonstationary clutter for airborne radar with conformal antennas array (CAA) is analyzed,and an angle-Doppler compensation method for clutter suppression is proposed.Compared with the uniform linear antennas array (LAA),the clutter angle-Doppler spectrum distribution of CAA should be described in four-dimension space,i.e.,three-dimension space and time,because the elements of CAA are distributed in three-dimension space.According to this theory,the clutter nonstationarity for airborne radar with CAA is firstly analyzed and the nonstationary nature of it is given.Then an improved STAP method for airborne radar with CAA is proposed,and it compensates for the mainlobe clutter in four-dimension frequency domain.Finally,the simulation results show the effect of the novel method.     

Reduced-rank space-time adaptive detection for airborne radar

For airborne radar,there are usually insufficient independent and identically distributed(IID)training data because of geometric considerations and terrain variations.The rank reduction technique is one of the most effective approaches to circumvent this problem.In this study,we investigate four reduced-rank spacetime adaptive detectors for airborne radar,namely,the reduced-rank sample-matrix-inversion(RR-SMI),the reduced-rank adaptive matched filter(RR-AMF),the reduced-rank adaptive coherence estimator(RR-ACE),and the reduced-rank generalized likelihood ratio test(RR-GLRT).Their asymptotic analytical probabilities of detection(PD’s)and false alarm(PFA’s)are all derived.These detectors all asymptotically attain a constant false alarm rate(CFAR).It is shown that these four reduced-rank detectors exhibit detection performance which is better than or comparable to that of two existing reduced-rank detectors,proposed by Reed and Gau(RG1and RG2).Moreover,these four reduced-rank detectors are more robust to change in power of clutter and noise than RG1 and RG2.     

Improved eigenanalysis canceler based on data-independent clutter subspace estimation for space-time adaptive processing

The eigenanalysis canceler(EC)method which is suitable for nonhomogeneous clutter can suppress clutter efectively by discarding the eigenvectors of small eigenvalues,which is a well-known subspace-based space-time adaptive processing(STAP)method.However,the computational complexity of conventional EC STAP method is huge due to the eigenvalue decomposition.Moreover,the corresponding performance would be degraded signifcantly by the subspace leakage phenomenon,since the clutter subspace is not strictly confned to a low-rank subspace any more.Therefore,an improved EC STAP method based on the data-independent clutter subspace estimation is proposed to reduce the computational complexity,where the clutter subspace is rapidly constructed by sampling the prolate spheroidal wave functions(PSWF)non-uniformly.In order to deal with the subspace leakage phenomenon,the proposed EC-PSWF STAP method is modifed based on the covariance matrix taper(CMT)to obtain the covariance matrix by re-establishing the noise floor.The corresponding performance of proposed method is evaluated by using the numerical simulation.     

A fast-converging space-time adaptive processing algorithm for non-Gaussian clutter suppression

The normalized fractionally-lower order moment (NFLOM) algorithm differs from the normalized least mean square (NLMS) algorithm in that it minimizes the lower order moment (p<2) of the error rather than the variance (p=2). This paper first evaluates the performances of the NFLOM for space-time adaptive processing in heavy-tailed compound K clutters in terms of the excess mean square error (MSE), misalignment, beampatterns, and output signal-to-interference-and-noise-ratio (SINR). The results show that the MSE curve of a small-order NFLOM exhibits faster convergence but higher steady-state error than a large-order NFLOM. Second, this paper proposes a new variable-order FLOM algorithm to dynamically change the order during adaptation, thus achieving both fast initial convergence and low steady-state error. The new algorithm is applied to STAP for Gaussian and non-Gaussian clutter suppression. The simulation results show that it achieves the best compromise between fast convergence and low steady-state error in both types of clutters.     

利用先验信息的幅度-相位联合STAP样本选取方法

针对分立杂波和强目标信号等引起的机载雷达杂波非均匀特性,提出一种利用先验信息的幅度-相位联合选取样本的方法,利用杂波多普勒频率与入射角度之间的先验关系,将检测多普勒通道在特定角度的幅度最强的单元作为样本集,确保对分立杂波的对消深度,再通过相位检测方法剔除样本集中相位明显偏离相位期望的样本,克服样本集包含目标信号引起的目标自相消效应。通过某机载相控阵雷达的实测数据比较研究表明,该方法较传统样本选取方法更具优势,且计算量较小便于工程采用。     

Robust ionospheric clutter mitigation method for high frequency surface wave radar

This paper analyzes the characteristics of ionospheric clutter, which has proven to be the greatest impediment in high frequency surface wave radar （HFSWR）. Ionospheric clutter influences most range bins, all Doppler bins and beams, and exhibits a high directivity. A robust adaptive side-lobe cancellation algorithm based on orthogonal auxiliary beams, which use the difference beams to estimate the ionospheric clutter information, is introduced to suppress the ionospheric clutter. Training data was selected by excluding the sea clutter and probable target unit; and diagonal loading was added to enhance the robustness of the algorithm. Experimental results demonstrate that the algorithm is able to suppress the ionospheric clutter whilst preserving the desired characteristics of the target echoes and is suitable for practical implementation.     

机载MIMO雷达两级降维空时自适应处理方法

针对机载多输入多输出( Multiple-input multiple-output,MIMO)雷达杂波抑制问题,提 出一种两级降维空时自适应处理 方法。首先利用多普勒滤波对杂波信号进行时域降维处理;然后将空域发射 接收二维波束 形成权矢量重构为发射权矢量和接收权矢量Kronecker积形式,并将高维权矢量转化为两个 低维权矢量进行分别求解,最后进行权矢量合成。该算法能够有效降低训练样本数需求与运 算复杂度,在小样本条件下具有良好的杂波抑制性能,因此更具有实际应用价值。仿真结果 验证了算法的有效性。