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。     

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。     

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.     

基于主瓣杂波补偿的多约束直接数据域方法

在雷达天线应用的研究中,传统的直接数据域空时自适应处理算法在利用空域对消实现SOI滤除时,与SOI同方向的主瓣杂波也会被滤除掉,使得主瓣杂波得不到有效的抑制,从而影响输出信干噪比(SINR).为了有效抑制主瓣杂波,提出了一种对空域对消目标后的数据进行主瓣杂波补偿的算法,以有效抑制主瓣杂波,提高输出信干噪比.同时针对SOI方向估计有偏差的情况,采用多约束的方法来保护SOI不被抑制,并进行了仿真.仿真结果验证了算法的有效性.     

非均匀杂波环境下基于贝叶斯方法的自适应检测

对于非均匀杂波环境下信号自适应检测问题,由于待测数据样本的协方差矩阵与训练数据的协方差矩阵不相同,造成检测性能下降, 针对此问题本文提出了基于贝叶斯方法的广义似然比检测器(Bayesian generalized likelihood ratio test, B-GLRT). 通过对非均匀杂波环境下协方差矩阵间的关系进行统计建模,使在B-GLRT的设计过程中能够结合杂波的非均匀性, 并且这种非均匀性在统计模型中可以通过标量参数调节.同时通过对协方差矩阵选择合适的先验分布, 使B-GLRT能够融合有助于提高检测性能的先验知识. 通过仿真实验,验证了B-GLRT的检测性能高于传统的非贝叶斯检测器,并且分析了杂波环境非均匀性和先验信息对自适应检测性能的影响.     

杂波协方差矩阵结构的融合估计方法

针对特定杂波背景下的最优或次优杂波协方差矩阵估计方法难以适应过渡杂波环境的问题,提出协方差矩阵结构的融合估计方法,通过调整参数涵盖现有的3种杂波协方差矩阵估计方法,并分析所提出方法对应的自适应归一化匹配滤波器的自适应特性.其次,确定了控制参数的经验公式,经验公式符合数值结果.最后,从估计精度、恒虚警率特性和检测性能3个方面对所提出方法和已有方法进行对比分析.仿真结果表明,在过渡杂波环境中,所提出方法的精度更高、检测效果更好,对实际杂波非高斯程度时空渐变性具有较强的适应能力.     

小样本机载分布式雷达空时自适应处理方法

针对机载分布式雷达地杂波和干扰抑制的问题,提出一种基于小样本协方差重构的机载分布式雷达空时自适应处理（space-time adaptive processing,STAP）方法。该方法根据分布式雷达接收的杂波与干扰具有相关性不同的特点,首先利用小样本估计出各子雷达的杂波空时协方差矩阵和干扰全空域协方差矩阵,然后通过块对角延拓和时域拓展方法得到完整的空时协方差矩阵。所提方法利用全部空域自由度,在有效抑制杂波的同时抑制较多数量的干扰,克服了理想协方差矩阵估计时所面临的独立同分布（independent and identically distributed,i.i.d.）样本严重不足的问题。理论分析和仿真实验验证了所提方法的有效性。     

非高斯杂波协方差矩阵估计方法及CFAR特性分析

在独立同分布的球不变随机向量非高斯杂波背景下,针对采样协方差矩阵(SCM)、归一化采样协方差矩阵(NSCM)及相应的迭代估计矩阵(NSCM-RE),利用统一理论框架分析了3种不同估计矩阵对应的自适应归一化匹配滤波器(ANMF)的恒虚警率(CFAR)特性.理论分析表明,SCM-ANMF只对杂波归一化协方差矩阵具有CFAR特性;而NSCM-ANMF只对杂波功率水平具有CFAR特性;在有限次迭代下,NSCM-RE-ANMF对杂波归一化协方差矩阵不具有CFAR特性.为了改善ANMF的自适应特性,提出了一种自适应协方差矩阵估计方法(AE),并将其作为初始化矩阵进行迭代估计,在有限次迭代下,所获得的AE-RE-ANMF对杂波归一化协方差矩阵和功率水平均具有CFAR特性.最后,通过仿真验证了所提方法的有效性.     

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。     

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,...     

一种时域滑窗多普勒后处理的STAP方法

一般的多普勒后处理方法忽视了相邻多普勒通道间数据的相关性,这将造成杂波协方差矩阵估计的不准,因此本文提出了一种联合相邻多普勒通道信息的空时自适应处理方法.该方法先通过时域滑窗后滤波的处理方式来降低杂波的自由度,再利用相邻多普勒门上数据相关性进行联合处理,最终构造出新的杂波协方差矩阵.该方法在提高时域自适应能力的同时减轻了空域杂波相消的负担,提高了处理器的性能,并消除因信息泄露带来的影响,以达到有效抑制地杂波的目的.通过对仿真和实测数据处理,证明了该方法的有效性.     

Enhanced knowledge-aided space–time adaptive processing exploiting inaccurate prior knowledge of the array manifold

The accuracy of the prior knowledge of the clutter environments is critical to the clutter suppression performance of knowledge-aided space–time adaptive processing (KA-STAP) algorithms in airborne radar applications. In this paper, we propose an enhanced KA-STAP algorithm to estimate the clutter covariance matrix considering inaccurate prior knowledge of the array manifold for airborne radar systems. The core idea of this algorithm is to incorporate prior knowledge about the range of the measured platform velocity and the crab angle, and other radar parameters into the assumed clutter model to obtain increased robustness against inaccuracies of the data. It first over-samples the space–time subspace using prior knowledge about the range values of parameters and the inaccurate array manifold. By selecting the important clutter space–time steering vectors from the over-sampled candidates and computing the corresponding eigenvectors and eigenvalues of the assumed clutter model, we can obtain a more accurate clutter covariance matrix estimate than directly using the prior knowledge of the array manifold. Some extensions of the proposed algorithm with existing techniques are presented and a complexity analysis is conducted. Simulation results illustrate that the proposed algorithms can obtain good clutter suppression performance, even using just one snapshot, and outperform existing KA-STAP algorithms in presence of the errors in the prior knowledge of the array manifold.     

Robust space time processing based on bi-iterative scheme of secondary data selection and PSWF method

Prolate spheroidal wave function (PSWF) method could improve the target detection performance for space-time adaptive processing (STAP) in nonhomogeneous environment. However, it may be ineffective with system parameter error. In this paper, we correct the system parameter with clutter spectrum analysis. Since contaminated samples contained in the secondary data set have detrimental impact on this spectrum analysis, the traditional sample selection method of generalized inner production (GIP) is combined with PSWF method, and then a bi-iterative scheme is proposed. Firstly, the system parameter for PSWF is estimated via the analysis of spectrum image, which is constructed with the secondary data set. Then, the covariance matrix is derived by PSWF method with the estimated parameter. Thirdly, the GIP sample selection technique is implemented with the PSWF covariance matrix, and the secondary data set would be updated. Repeat these steps until a stable parameter is obtained. Several vital issues such as how to estimate the parameter with real data and why the precision of covariance matrix could be improved during the iteration are analyzed. In the end, the validity of the proposed algorithm is substantiated by practical and simulation results.     

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.     

低复杂度递推信道估计联合均衡算法

精确的信道状态信息对单载波频域均衡(Sin gle carrier frequency domain equalization, SC FDE)非常重要,本文基于高斯消息传 递 (Gaussian message passing,GMP)提出了一种递推最小二乘(Least squares, LS)信道估计 算法。借助于因子图,根据广义分配率思想,将估计函数分为多个局部函数,每个局部函数 做多利处理,然后通过定义辅助变量使其成递推关系。根据turbo原理,迭代交换软信息, 使得估计、均衡及译码联合进行。在此基础上 ,分析并推导出无偏简化方法,然后借助于快速傅里叶变换,使得算法复杂度随观察向量长 度的增加呈对数上升。仿真表明该简化算法具有较好的信道估计性能和误码率特性的同时, 显著降低了计算量。     

基于MUSIC谱估计的探地雷达回波处理方法

为了克服现有信号处理算法对探地雷达噪声及杂波滤除的不足,提出了一种基于MUSIC谱估计时域与频域联合滤波方法。利用常用的去直流波和均值滤波抑制方法进行雷达信号预处理,在此基础上,引入谱分析中信号子空间与噪声子空间的概念,将MUSIC算法与最小二乘法相结合来进行探地雷达杂波及噪声处理。仿真及实验结果表明,提出的回波处理方法可以有效地抑制雷达信号中的噪声和杂波,能提高测量数据的精度,具有较高的实用性和可用性。