基于杂波子空间估计的MIMO雷达降维STAP研究

 多输入多输出(MIMO)雷达是近年来出现的一种新体制雷达,针对MIMO体制的机载雷达开展空时自适应处理（STAP）技术研究是值得进一步努力的方向。本文研究了机载MIMO雷达STAP技术的降维算法,通过对STAP技术杂波抑制原理进行分析,推导并得到一种基于杂波子空间的降维算法。结合扁长椭球波函数（PSWF）的特点,提出了一种基于杂波子空间估计的降维算法,并与若干降维算法的杂波抑制性能进行比较。结果表明,当存在阵元幅相误差时,该算法在保持杂波抑制性能的同时能够有效地降低STAP算法的运算量。     

基于稀疏贝叶斯学习的字典失配杂波空时谱估计方法

杂波谱稀疏恢复空时自适应处理（STAP）是一种有效减少杂波样本数需求的机载雷达杂波抑制方法。然而,空时平面被离散地划分为若干个网格点来构建空时导向矢量字典,当字典在失配时,杂波脊不能准确落在预先离散化的网格点上,稀疏恢复STAP性能严重下降。提出了一种基于稀疏贝叶斯学习的字典失配杂波空时谱估计方法,首先利用二维泰勒级数建立空时动态字典模型,然后将字典失配误差作为待估超参数构建贝叶斯稀疏恢复模型,并利用失配误差估计值对空时导向矢量字典进行修正,最后利用修正后的空时导向矢量字典重构杂波协方差矩阵,进而计算杂波空时谱。实验证明,该方法能够有效提高字典失配情况下的杂波谱稀疏恢复精度,杂波抑制性能优于已有字典预先离散化的稀疏贝叶斯学习STAP方法。     

波形分集MIMO STAP雷达的灵敏度(英文)

空时自适应处理(STAP)是一种有效的机载雷达地杂波抑制方法,多输入多输出(MIMO)雷达是一种性能优于传统雷达的新体制雷达,近期STAP被应用于MIMO雷达以提高性能。MIMO雷达所发射的波形可以彼此相关或不相关。针对波形分集MIMO雷达的STAP处理,提出了一种统一的信号模型,基于此模型,将STAP的性能表示为波形协方差矩阵(WCM)的函数。对波形的影响进行了研究,证明灵敏度(即最大可检测距离)与WCM的最大特征值成正比。理论分析和仿真结果验证了波形对MIMO STAP雷达灵敏度的影响,相关结论可应用于波形的折中设计,以获得最佳系统性能。     

基于稀疏阵列和码分信号的机载预警雷达STAP研究

基于稀疏阵列和码分正交信号,研究了机载雷达的空时自适应处理（STAP）技术,用于空中预警背景下的地面杂波抑制和运动目标探测。提出了稀疏阵列码分多相位中心孔径综合方法,采用正交编码信号实现多发多收,使综合后不同编码信号的相位中心在数量和分布情况上和满阵天线的相同,从而避免了稀疏阵列天线旁瓣较高的问题;在孔径综合的基础上,利用空时自适应处理方法完成杂波抑制,实现运动目标检测。仿真结果表明了本文方法的有效性。     

未知互耦条件下混合信号波达方向估计

阵列天线互耦对导向矢量的扰动以及信号相干性对数据协方差矩阵造成的秩损,使得基于子空间正交性原理的超分辨波达方向估计(Direction-of-Arrival,DOA)算法性能恶化,甚至失效。针对这一问题,提出一种在相干与非相干信号混合状态下无需阵列互耦补偿的特征矢量平滑DOA估计算法。该算法对部分阵元接收数据的协方差矩阵特征分解,将得到的特征矢量平滑处理后构造等效协方差矩阵,抑制阵列互耦影响的同时完成混合信号DOA估计。在阵列互耦和信号相干性均未知的条件下,正确估计了信号DOA,无需互耦参数估计或补偿。计算机仿真结果验证了算法的有效性。     

圆台阵列杂波模型及空时二维自适应处理

针对圆台共形阵列,建立了空时二维自适应处理(STAP)的杂波模型,给出了圆台阵列杂波抑制最优权值的计算方法。在此基础之上,为了实现可应用到实际环境中的自适应处理方法,进一步讨论了将局部联合域(JDL)降维算法推广至圆台阵列中的问题。得出了圆台阵列JDL算法降维变换矩阵的表达形式,研究了参考波束的数目选取、波束指向等因素对降维损失的影响。理论分析以及仿真结果表明,通过合理选择通道数、波束方位向指向间隔等参数,该算法能够减少自适应波束形成的计算量,而且可以用较少的训练样本获得较好的处理性能。     

非理想均匀圆阵的波达方向和多普勒频率联合估计

针对均匀圆阵存在一般阵列误差 (如阵元的幅相误差和安装位置误差等 )的情况 ,提出了多个信号的波达方向和多普勒频率估计方法。直接利用均匀圆阵的阵列流形 ,采用波达矩阵法估计各个信号的多普勒频率。由一般阵列误差的统计特性构造加权矩阵 ,采用加权总体最小二乘法估计各个信号的波达方向。此方法具有鲁棒性强等特点。计算机仿真证明了此方法的有效性     

Estimation of DOA and Doppler Frequency on Nonideal UCA

针对均匀圆阵存在一般阵列误差(如阵元的幅相误差和安装位置误差等)的情况，提出了多个信号的波达方向和多普勒频率估计方法。直接利用均匀圆阵的阵列流形，采用波达矩阵法估计各个信号的多普勒频率。由一般阵列误差的统计特性构造加权矩阵，采用加权总体最小二乘法估计各个信号的波达方向。此方法具有鲁棒性强等特点。计算机仿真证明了此方法的有效性。     

基于FPGA的自适应阵列协方差矩阵特征分解的实现

卫星导航抗干扰的过程中，对空间信号波达方向估计、干扰个数检测、最优权矢量的求解直接影响着导航接收机的抗干扰性能，而协方差矩阵的特征分解是这些算法实现的核心部分。根据自适应阵列天线获得的协方差矩阵的特性，基于双边并行Jacobi算法，实现了基于FPGA的协方差矩阵特征值和特征向量的求解，并通过在信号波达方向估计的应用进行了验证。另外，在实现的过程中对直接调用CORDIC IP核的方式进行了精度误差分析，并用一种双精度浮点的方式进行修正，提高了矩阵特征分解FPGA的实现精度，为导航抗干扰接收机性能的提升提供了有效的工程基础。     

参考阵元可自适应调整的阵列天线抗干扰方法

针对导航接收机抗干扰阵列天线在某些干扰来向存在抗干扰效果减弱的问题,建立了基于功率倒置算法的阵列信号处理模型,提出一种参考阵元可自适应调整的阵列天线抗干扰方法.以四阵元均匀圆阵为例进行了仿真验证,仿真结果表明,相比于参考阵元固定的功率倒置算法,此方法能够很好地解决部分干扰来向抗干扰效果减弱的问题.     

Robust space-time adaptive processing for airborne radar in nonhomogeneous clutter environments

Space-time adaptive processing (STAP) holds tremendous potential for the new generation airborne surveillance radar, in which the phased array antennas and pulse Doppler processing mode are adopted. A new STAP approach using the multiple-beam and multiple Doppler channels is presented here for airborne phased array radar. The approach with space-time multiple-beam (STMB) architecture is robust to array errors and has very low system degrees of freedom (DOFs). Hence, it has low sample support requirement and it is very suitable for the practical planar phased array radar under nonhomogeneous clutter environments. Meanwhile, a new nonhomogeneous detector (NHD) based on the correlation dimension (CD) is also proposed here, which is used as an effective method to screen tracing data prior to detection processing. It can further improve the performance of the STAP approach in the severely nonhomogeneous clutter environments. Therefore, a scheme that incorporates the correlation dimension nonhomogeneity detector (CD-NHD) with the STMB is recommended, which we term CD-NHD-STMB. The experimental simulation results indicate that: 1) the STMB processor is robust to array element error and has high performance under nonhomogeneous clutter environments; 2) the CD-NHD is also effective on the nonhomogeneous clutter. As a result, the CD-NHD-STMB scheme is robust to array element error and nonhomogeneous clutter, and therefore available for airborne phased array radar applications.     

Performance improvement for constellation SAR using signal processing techniques

A new concept of spaceborne synthetic aperture radar (SAR) implementation has recently been proposed - the constellation of small spaceborne SAR systems. In this implementation, several formation-flying small satellites cooperate to perform multiple space missions. We investigate the possibility to produce high-resolution wide-area SAR images and fine ground moving-target indicator (GMTI) performance with constellation of small spaceborne SAR systems. In particular, we focus on the problems introduced by this particular SAR system, such as Doppler ambiguities, high sparseness of the satellite array, and array element errors. A space-time adaptive processing (STAP) approach combined with conventional SAR imaging algorithms is proposed which can solve these problems to some extent. The main idea of the approach is to use a STAP-based method to properly overcome the aliasing effect caused by the lower pulse-repetition frequency (PRF) and thereby retrieve the unambiguous azimuth wide (full) spectrum signals from the received echoes. Following this operation, conventional SAR data processing tools can be applied to focus the SAR images fully. The proposed approach can simultaneously achieve both high-resolution SAR mapping of wide ground scenes and GMTI with high efficiency. To obtain array element errors, an array auto-calibration technique is proposed to estimate them based on the angular and Doppler ambiguity analysis of the clutter echo. The optimizing of satellite formations is also analyzed, and a platform velocity/PRF criterion for array configurations is presented. An approach is given to make it possible that almost any given sparse array configuration can satisfy the criterion by slightly adjusting the PRF. Simulated results are presented to verify the effectiveness of the proposed approaches.     

Sensitivity analysis of the maximum likelihood direction-findingalgorithm

A first-order analysis is performed of the sensitivity of the maximum likelihood (ML) direction-finding algorithm to system errors which cause differences between the array manifold used by the algorithm and the true array manifold. The effect of such errors on the directions-of-arrival (DOA) estimates is investigated. The ability of the ML algorithm to resolve two closely spaced sources in the presence of phase and gain errors in the array elements or in the receivers, or errors in the element locations, is analyzed. A formula for computing the failure threshold of the algorithm as a function of source separation and other system parameters is derived and tested by simulation. The analysis assumes that the exact covariance matrix of array element outputs is known     

STAP with medium PRF mode for non-side-looking airborne radar

Space-time adaptive processing (STAP) has been widely discussed for airborne radar systems to improve the system performance of detecting targets. This is especially true for airborne early warning (AEW) radar, which should find long-range and small radar cross section (RCS) targets such as the stealth aircraft and missiles. However, in existing airborne radar literature, STAP is mainly considered for clutter and jamming rejection in side-looking airborne radar (SLAR) applications. There have been fewer discussions on airborne radar with non-side-ways looking array radar (non-SLAR). The STAP of non-SLAR such as forward looking array radar is also very important and can not be avoided for airborne radar to detect targets in all directions. The STAP of the non-SLAR is studied here. A scheme has been proposed, which is processed by the way of STAP combined with multiple staggered medium pulse repetition frequencies (PRFs). We further study the selection of PRFs in order to make the scheme more available for non-SLAR radar. We analyze two typical non-SLAR cases, i.e., inclined-sideways looking array and forward looking array. We examine this scheme by comparing the performances of three processing systems under the criteria of range-velocity blind zone minimization. Computer simulation results show the multiple-PRFs STAP scheme is feasible for non-SLAR and can be applied to phased-array AEW radar systems     

Effects of mutual coupling on the performance of STAP antennaarrays

The effect of mutual coupling on the performance of space-time adaptive processing (STAP) antenna arrays is investigated. A signal model that includes the effects of mutual coupling is derived and used to compute the optimum solution for the fully adaptive and a variety of partially adaptive algorithms. The simulations indicate that if the mutual coupling is not properly accounted for there is significant degradation of the signal-to-interference-plus-noise ratio (SINR). In addition, the clutter notch is widened resulting in a larger minimum detectable velocity (MDV) of the target. When the mutual coupling is properly accounted for, the performance can be restored to the ideal level. However, STAP algorithms, in general, are very sensitive to errors in the mutual coupling matrix, requiring a very complete knowledge of this matrix for good performance. Of all the algorithms considered here, beam space algorithms appear to be the most robust with respect to uncertainties in the mutual coupling matrix     

Reduced-rank STAP for high PRF radar

Due to the range ambiguity of high pulse-repetition frequency (HPRF) radars, echoes from far-range fold over near-range returns. This effect may cause low Doppler targets to compete with near-range strong clutter. Another consequence of the range ambiguity is that the sample support for estimating the array covariance matrix is reduced, leading to degraded performance. It is shown that space-time adaptive processing (STAP) techniques are required to reject the clutter in HPRF radar. Four STAP methods are studied in the context of the HPRF radar problem: low rank approximation sample matrix inversion (SMI), diagonally loaded SMI, eigencanceler, and element-space post-Doppler. These three methods are evaluated in typical HPRF radar scenarios and for various training conditions, including when the target is present in the training data     

Circular array STAP

Traditionally, space-time adaptive processing (STAP) for airborne early warning (AEW) radar has been applied to uniform linear arrays (ULAs). However, when considering the overall radar system, electronically scanned circular arrays have advantages: a better combination of even and continual angular and temporal coverage, and mechanical simplicity because it does not need to rotate. This paper answers the question “How well does STAP perform when applied to a circular array?” This paper shows that for the AEW mission, circular arrays are indeed STAP compatible. However, when conventional STAP algorithms are used there may be a small loss in performance when compared with a ULA. With some care in the choice and implementation of the STAP algorithm, the majority of the degradation is at close ranges, where the target returns are relatively strong. At long ranges performance is barely affected. A STAP algorithm which compensates for the circular array environment and provides better performance than existing algorithms is presented