一种机载雷达子阵杂波谱估计方法

针对机载雷达空时自适应处理技术在实际应用中存在的问题和现有杂波谱估计方法不足的情况,提出一种基于空时二维线性预测的子阵杂波谱估计方法.该方法通过对接收到的空时杂波数据进行抽取和排列,以及对杂波协方差矩阵进行重新构造的方法,求取空时二维线性预测权值,然后进行杂波谱估计.仿真结果表明,本文方法在理想情况、存在阵元误差和速度模糊下均能够准确估计出杂波谱;与传统最小方差谱相比,谱峰更尖锐且减少了杂波谱估计的计算量.     

双基机载雷达的杂波谱补偿方法

双基机载雷达结构中杂波多普勒频率是随距离和双基结构发生变化的,使杂波谱在距离上呈现严重的非平稳性,导致杂波协方差矩阵估计困难．针对此问题提出一种基于最小二乘的空域导向矢量拟合的杂波谱补偿方法,先将训练单元上的空域导向矢量向待检单元的空域导向矢量投影得到一个变换矩阵,用这个矩阵对训练单元上的数据进行预处理,消除杂波谱的非平稳性,得到足够的统计数据,从而构造新的杂波协方差矩阵,最后利用空时自适应算法对补偿后的数据进行杂波抑制．仿真结果表明该方法能够有效地抑制地杂波的非平稳性,提高雷达杂波抑制的性能．     

贝塞尔级数展开的机载前视阵雷达杂波谱补偿方法

提出了基于级数展开的杂波谱补偿方法,首先采用贝塞尔函数对每个距离环实现方位和俯仰角的分离;接着将训练单元的俯仰矢量向待检单元进行投影,得到一个变换矩阵.再使用变换矩阵对给定的多普勒门的数据进行处理,减轻杂波谱对距离的依赖性;最后,从变换后的数据中估计杂波协方差矩阵,再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,该方法能有效改善杂波的非平稳性,提高了系统抑制杂波的性能.     

机载雷达非正侧面阵近程杂波抑制新方法

根据机载雷达非正侧面阵的近程杂波随距离快变的特性,提出了俯仰向空域滤波子阵合成的算法．该算法利用平面阵列天线在俯仰向的自由度,先对近程杂波进行滤除,然后再在方位空域和时域做空时自适应处理．这样就增强了远程杂波的距离平稳性,很容易准确地估计协方差矩阵,从而提高了空时自适应处理抑制非正侧面阵杂波的性能．仿真结果证明了无误差时改善因子可以提高约30dB．     

降维STAP中稀疏恢复的角度多普勒通道选择方法

在机载雷达信号处理中,高强度的地杂波严重影响信号检测性能,而空时自适应处理(STAP)是一种有效抑制杂波的技术。实际处理中,由于杂波的非均匀性,空时自适应处理往往面临着可用有效样本数较少的问题,同时机载雷达处理的信号维度极为庞大。为了解决这些问题,提出了一种基于稀疏恢复的降维STAP通道选择方法。利用少量样本通过稀疏恢复的方法估计出全维度的杂波协方差矩阵(CCM),并以此为依据评估各个通道的重要性,选择合适的通道构造出降维后的杂波协方差矩阵并进行STAP处理,解决了有效样本较少的问题,同时保证了降维算法的性能。数值仿真验证了算法有效且比典型的稀疏STAP算法效果更好,讨论了在不同样本数下,输出性能与通道数的关系,结论具有工程应用意义。     

基于矩阵填充的互质阵列欠定DOA估计方法

为了解决现有基于互质阵列的DOA估计方法舍弃差联合阵列中非均匀虚拟阵元而导致最大可估计信号数损失的问题,提出了一种基于矩阵填充的DOA估计方法。首先,根据差联合阵列与波程差一一对应的特性,构造一个部分元素缺失的Toeplitz化的阵列协方差矩阵,建立了基于矩阵填充的DOA估计模型,并验证了该模型满足零空间性质;然后,根据低秩矩阵填充理论,将DOA估计问题转化为矩阵核范数最小化问题进行求解,通过不定点延续算法将该协方差矩阵中的零元素进行填充恢复为完整协方差矩阵;最后,对协方差矩阵进行奇异值分解,转化为多项式求根,得到DOA的估计。仿真实验结果验证了本文方法的有效性和优越性。实验结果表明,本文方法能够对差联合阵列中的空洞部分进行有效填充,增加了可利用的阵列自由度,提高了可估计信号数,同时能够有效避免传统稀疏重构算法中由于角度域离散化导致的基不匹配问题对估计性能的影响,提高了估计精度和分辨力。     

星载双基地雷达空时二维杂波建模和特性分析

建立了星载双基地雷达杂波模型,考虑了卫星椭圆轨道、地球表面弯曲、地球自转、距离模糊以及收发几何配置等因素对杂波的影响．基于该模型对星载双基地雷达杂波的空时二维特性及其对空时二维自适应处理(STAP)性能的影响进行了仿真分析,结果表明,星载双基地雷达的杂波分布具有空变性和时变性; 这些特性以及距离模糊都会导致系统抑制杂波性能的下降．另外,当接收阵列阵元都放置在一颗卫星上时,由于接收阵列的低空间分辨率导致难以检测低速目标,仿真结果表明,将接收阵列空间超稀疏分布(分布式小卫星)可以很好的解决这一问题．     

一种改进的机载双基雷达多普勒频移算法

机载双基雷达由于载机之间的相对运动,造成其杂波分布随距离变化而变化,直接导致杂波协方差矩阵估计不准确,进而使得空时自适应处理技术的杂波抑制和动目标检测性能下降.提出一种杂波非均匀补偿技术,该方法先采用多普勒频移预处理,使得主波束方向上的杂波谱重合,然后通过对多个空间角方向上的归一化多普勒频率进行补偿,使各距离单元的杂波谱保持一致.仿真结果表明该方法能有效改善机载双基雷达的杂波非均匀情况,性能明显优于原有算法.     

一种特殊阵列实现DOA估计的方法

提出了一种基于特殊阵列形式实现DOA估计的方法,在均匀线性阵列（Uniform Linear Array,ULA）上增加一个阵元,将阵元放在特定的位置,得到一组特殊的阵元组合,打破了均匀线阵中导向矢量的周期性,成功地避免了角度估计模糊.此外,该方法在同等硬件资源的条件下,得到了更多阵元组合,可以获得更高的阵列方位分辨率.提出了一种适用于新阵列结构的矩阵分块空间平滑算法,利用其导向矢量的局部周期性将其数据协方差矩阵分块空间平滑后,重新组合得到修正后的数据协方差矩阵,实现了相干信号源的DOA估计.     

一种特殊阵列实现DOA估计的方法

提出了一种基于特殊阵列形式实现DOA估计的方法，在均匀线性阵列（ Uniform Linear Array, ULA）上增加一个阵元，将阵元放在特定的位置，得到一组特殊的阵元组合，打破了均匀线阵中导向矢量的周期性，成功地避免了角度估计模糊．此外，该方法在同等硬件资源的条件下，得到了更多阵元组合，可以获得更高的阵列方位分辨率．提出了一种适用于新阵列结构的矩阵分块空间平滑算法，利用其导向矢量的局部周期性将其数据协方差矩阵分块空间平滑后，重新组合得到修正后的数据协方差矩阵，实现了相干信号源的DOA估计.     

Fast space-time adaptive processing method by using the sparse representation

One of the key problems of space-time adaptive processing (STAP) is how to estimate the clutter covariance matrix (CCM) accurately with a small number of samples when the clutter environment is heterogeneous. The CCM estimation methods based on sparse representation (CCM-SR) can achieve a good estimation performance with only one or a few samples, which significantly improves the convergence rate of the STAP. By using the sparsity characteristic of the clutter spectrum, the CCM-SR method estimates the clutter spectrum and yields a good estimation of the CCM. However, there are often many pseudo-peaks in the clutter spectrum estimated by the sparse representation (SR), which will cause a CCM estimation error. By exploiting the special relationship of the clutter ridge curve between space domain and Doppler domain, we can eliminate the pseudo-peaks in the clutter spectrum effectively via fitting the curve of the clutter ridge and improve the estimation accuracy of the CCM. In addition, a byproduct of our method is the estimation of the flying parameters (the velocity of the radar platform, the crab angle and so on). Experimental results show that the proposed method can improve the performance of conventional STAP based on sparse representation (STAP-SR) and obtain a good estimation of the flight parameters.     

Robust registration based compensation method based on sparse recovery

The loss of the degree of system freedom and mismatch of prior information based on the registration based compensation (RBC) method will result in a decline in computational performance. In order to solve above problems, a robust method named SRBC is proposed where the sparse recovery method is applied instead of the sub-aperture smoothing operation. First, the SRBC utilizes the sparse recovery to get the super resolution clutter space-time spectrum. Then the transition covariance matrix is calculated. Finally, the clutter covariance matrix is reconstructed by the Capon spectrum. Compared with the traditional RBC method, the SRBC proposed does not depend on sub-aperture smoothing operation and prior information and maintains the performance of clutter range-dependence. In addition, it is also steadier when the sensor error exists. Experimental simulation demonstrates the correctness and feasibility of this method.     

Detection of the airborne MIMO radar moving target in the non-Gaussian clutter

Due to the moving platforms, the clutters in distributed airborne MIMO radar are non-Gaussian and non-homogeneous, which leads to having no independent and identically distributed training data to estimate the clutter covariance matrix. To solve the problem, we propose that the covariance of the clutter should be modeled as an inverse complex Wishart distribution whose average value is a Hadamard product of the covariance matrix taper (CMT) and the clutter Doppler spectrum component. Based on this clutter model, a novel detector combing the Bayesian approach and the generalized likelihood ratio test(GLRT) is proposed. Numerical simulation results show that the proposed detector has a better detection performance compared with two current commonly used non-Bayesian detectors.     

Registration based compensation method based on adaptive subspace tracking

The registration based compensation (RBC) method is an effective method to compensate the range-dependence of the main-lobe clutter and side-lobe clutter in the same time. However, the compensation performance of the RBC degrades because of the mismatch of prior information and the loss of degree of system freedom. Moreover, the RBC is not very suited for real-time implementation because of the enormous computational complexity and memory usage of eigenvalue decomposition. In this paper, a novel clutter range-dependence compensation method using the modified maximum likelihood adaptive subspace estimation algorithm, which is named the MRBC method for short, is proposed. The eigenvectors matrix and eigenvalues matrix of the clutter covariance matrix are estimated by iterative tracking instead of temporal and spatial smoothing, spectrum calculation and eigenvalue decomposition. Compared with the traditional RBC method, the proposed method can reduce the computational complexity significantly and maintain the performance of clutter range-dependence compensation. In addition, the proposed method can also achieve good performance when the system error exists because of no use of prior information. Experimental simulations demonstrate the validity of this method.     

非平稳噪声下稀疏表示的DOA估计算法

为提高非平稳噪声下远场非相干窄带信号波达方向（DOA）的估计精度,提出了一种基于稀疏重构的DOA估计算法.采用类协方差差分算法构造差分矩阵,抑制非平稳噪声的影响;基于类旋转不变子空间参数估计算法基本原理构造稀疏表示模型与权函数;利用加权l₁范数对模型求解,实现DOA估计.仿真结果表明,与传统的协方差差分算法、噪声协方差矩阵估计算法、秩迹最小化算法以及稀疏重构算法相比,所提算法不仅能较好地抑制非平稳噪声的影响,而且在低信噪比、低快拍数情况下具有较强的稳健性和较高的估计精度.     

基于均匀面阵的相干信号源二维DOA估计新方法

相干信号源的来波方向(Direction of Arrival,DOA)估计问题一直是阵列信号处理技术的一个研究热点。现实环境中的信号源多为相干的,针对此问题提出一种改进的相干信号源二维DOA估计算法。通过对接收信号协方差矩阵进行矩阵重构处理,将采样数据协方差矩阵的秩恢复到等于信号源的个数;对重构的协方差矩阵进行特征分解,构造出信号子空间矩阵和噪声子空间矩阵,将两个子空间矩阵联合构造出新的空间谱函数;基于新的空间谱函数进行二维谱峰搜索,即可估计出多个入射信号的二维DOA。使用计算机软件进行仿真验证,仿真结果表明了所提方法的有效性。     

基于Toeplitz矩阵初值的协方差估计方法

随着雷达分辨率的提高海杂波的分布已经不符合高斯分布,传统的检测器在非高斯环境下的性能也大幅下降.通过非高斯海杂波概率密度函数的似然函数推导出一个海杂波协方差矩阵的原始构型,通过解该协方差矩阵,提出了在非高斯海杂波环境下采用初值为Toeplitz矩阵的固定点协方差矩阵估计方法(T-FP),经过实测海杂波数据下的性能仿真结...