Comparison between monostatic and bistatic antenna configurationsfor STAP

The unique characteristics of bistatic radar operation on the performance of airborne/spaceborne moving target indicator (MTI) radars that use space-time adaptive processing (STAP) are discussed. It has been shown that monostatic STAP radar has the following properties. 1) For a horizontal flight path and a planar Earth the curves of constant clutter Doppler (isodops) are hyperbolas. 2) For a sidelooking antenna geometry the clutter Doppler is range independent. 3) Clutter trajectories in the cosφ-F plane (F=normalized Doppler) are in general ellipses (or straight lines for a sidelooking array). We demonstrate that these well-known properties are distorted by the displacement between transmitter and receiver in a bistatic configuration. It is shown that even for the sidelooking array geometry the clutter Doppler is range-dependent which requires adaptation of the STAP processor for each individual range gate. Conclusions for the design of STAP processors are drawn     

一种机载雷达两级降维空时二维自适应处理方法

 提出了一种机载相控阵雷达杂波抑制的两级降维空时自适应处理(STAP)方法,即：先根据杂波分布先验信息进行空时局域化(JDL)降维处理,然后对局域化输出进行多级维纳滤波(MWF),实现二次降维。该方法综合了固定结构和自适应结构降维技术的优点,将JDL处理引入到MWF中,从而有效降低MWF的杂波自由度。计算机仿真和理论分析表明本文方法比JDL自适应处理方法和全空时MWF方法具有更小的运算量,对阵元随机幅相误差具有很好的容差能力,是一种稳健的两级降维自适应处理方法。最后,基于仿真和实测数据的实验验证了算法的有效性。     

Airborne/spacebased radar STAP using a structured covariance matrix

It is shown that partial information about the airborne/spacebased (A/S) clutter covariance matrix (CCM) can be used effectively to significantly enhance the convergence performance of a block-processed space/time adaptive processor (STAP) in a clutter and jamming environment. The partial knowledge of the CCM is based upon the simplified general clutter model (GCM) which has been developed by the airborne radar community. A priori knowledge of parameters which should be readily measurable (but not necessarily accurate) by the radar platform associated with this model is assumed. The GCM generates an assumed CCM. The assumed CCM along with exact knowledge of the thermal noise covariance matrix is used to form a maximum likelihood estimate (MLE) of the unknown interference covariance matrix which is used by the STAP. The new algorithm that employs the a priori clutter and thermal noise covariance information is evaluated using two clutter models: 1) a mismatched GCM, and 2) the high-fidelity Research Laboratory STAP clutter model. For both clutter models, the new algorithm performed significantly better (i.e., converged faster) than the sample matrix inversion (SMI) and fast maximum likelihood (FML) STAP algorithms, the latter of which uses only information about the thermal noise covariance matrix.     

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。     

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3DT的空时自适应单脉冲参数估计算法

空时自适应处理(STAP)是机载预警雷达抑制杂波和干扰的一项关键技术,而多普勒三通道联合自适应处理(3DT)是适合工程实现的降维(RD)STAP方法。STAP目标检测后还需进一步估计目标的角度参数,因此将自适应单脉冲(AM)技术引入3DT,提出了一种高精度联合估计目标速度与方位空间角的空时自适应单脉冲算法。理论分析与仿真实验结果表明,当目标多普勒频率偏离检测多普勒单元中心频率时,该算法能同时减少目标多普勒跨越损失和空时导引矢量失配损失,进而提高输出信杂噪比(SCNR),改善目标测角精度。     

An approach to knowledge-aided covariance estimation

This paper introduces a parametric covariance estimation scheme for use with space-time adaptive processing (STAP) methods operating in heterogeneous clutter environments. The approach blends both a priori knowledge and data observations within a parameterized model to capture instantaneous characteristics of the cell under test (CUT) and reduce covariance errors leading to detection performance loss. We justify this method using both measured and synthetic data. Performance potential for the specific operating conditions examined herein include: 1) averaged behavior within roughly 2 dB of the optimal filter, 2) 1 dB improvement in exceedance characteristic relative to the optimal filter, highlighting improved instantaneous capability, and 3) impervious ness to corruptive target-like signals in the secondary data (no additional signal-to-interference-plus-noise ratio (SINK) loss, compared with 10 dB or greater loss for the standard STAP implementation), with corresponding detections comparable to the optimal filter case     

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     

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.     

Adaptive cancellation method for geometry-induced nonstationary bistatic clutter environments

This paper describes and characterizes a new bistatic space-time adaptive processing (STAP) clutter mitigation method. The approach involves estimating and compensating aspects of the spatially varying bistatic clutter response in both angle and Doppler prior to adaptive clutter suppression. An important feature of the proposed method is its ability to extract requisite implementation information from the data itself, rather than rely on ancillary - and possibly erroneous or missing - system measurements. We justify the essence of the proposed method by showing its ability to align the dominant clutter subspaces of each range realization relative to a suitably chosen reference point as a means of homogenizing the space-time data set. Moreover, we numerically characterize performance using synthetic bistatic clutter data. For the examples considered herein, the proposed bistatic STAP method leads to maximum performance improvements between 17.25 dB and 20.75 dB relative to traditional STAP application, with average improvements of 6 dB to 10 dB.     

机载MIMO雷达空时自适应杂波对消器

针对机载多输入多输出(MIMO)雷达杂波分布呈现空时耦合特性,提出一种空时自适应杂波对消器.利用机载MIMO雷达的脉冲回波数据,构造杂波对消器的系数矩阵.通过空时自适应杂波对消器的预处理,可以有效地抑制杂波,并通过与常规空时处理算法的级联,最终可以有效提高动目标的检测性能.实现了由传统地基雷达杂波对消器向机载运动平台的推广.仿真结果表明,这种自适应杂波对消器不仅适用于正侧视雷达,对于非正侧视雷达也同样适用.     

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。     

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     

Stap using knowledge-aided covariance estimation and the fracta algorithm

In the airborne space-time adaptive processing (STAP) setting, a priori information via knowledge-aided covariance estimation (KACE) is employed in order to reduce the required sample support for application to heterogeneous clutter scenarios. The enhanced FRACTA (FRACTA.E) algorithm with KACE as well as Doppler-sensitive adaptive coherence estimation (DS-ACE) is applied to the KASSPER I & II data sets where it is shown via simulation that near-clairvoyant detection performance is maintained with as little as 1/3 of the normally required number of training data samples. The KASSPER I & II data sets are simulated high-fidelity heterogeneous clutter scenarios which possess several groups of dense targets. KACE provides a priori information about the clutter covariance matrix by exploiting approximately known operating parameters about the radar platform such as pulse repetition frequency (PRF), crab angle, and platform velocity. In addition, the DS-ACE detector is presented which provides greater robustness for low sample support by mitigating false alarms from undernulled clutter near the clutter ridge while maintaining sufficient sensitivity away from the clutter ridge to enable effective target detection performance     

空时自适应处理张量波束成形器的外积合成法

为了解决空时自适应处理（Space-Time Adaptive Processing，STAP）对足量平稳训练快拍的要求，给出了一种设计STAP张量波束成形器的新算法——空时自适应处理张量子波束合成（TSS-STAP）法。分析表明：STAP中所需要的张量波束成形器，可首先在张量的各个子维度上分别进行子波束成形器的设计，然后再由张量的外积运算合成各子波束成形器而得到。进一步分析表明：由于本文算法可在较低自由度（DoF）的子维度上对张量波束成形器进行设计，因此降低了设计所需要的训练快拍数和计算复杂度，同时也实现了有效的去相关处理，使得其在非均匀杂波环境下有更好的目标检测性能。在仿真实验中，所提算法有效提升了目标检测结果，同时降低了目标检测所消耗的时间。     

Space-time autoregressive filtering for matched subspace STAP

Practical space-time adaptive processing (STAP) implementations rely on reduced-dimension processing, using techniques such as principle components or partially adaptive filters. The dimension reduction not only decreases the computational load, it also reduces the sample support required for estimating the interference statistics. This results because the clutter covariance is implicitly assumed to possess a certain (nonparametric) structure. We demonstrate how imposing a parametric structure on the clutter and jamming can lead to a further reduction in both computation and secondary sample support. Our approach, referred to as space-time autoregressive (STAR) filtering, is applied in two steps: first, a structured subspace orthogonal to that in which the clutter and interference reside is found, and second, a detector matched to this subspace is used to determine whether or not a target is present. Using a realistic simulated data set for circular array STAP, we demonstrate that this approach achieves significantly lower signal-to-interference plus noise ratio (SINR) loss with a computational load that is less than that required by other popular approaches. The STAR algorithm also yields excellent performance with very small secondary sample support, a feature that is particularly attractive for applications involving nonstationary clutter.     

MTI Optimization in a Multiple-Clutter Environment

A means of optimizing a moving target indicator (MTI) filter for rejecting several types of clutter, which are generated by different mechanisms such as by rain or the ground, is formulated. lt is found that the optimal performance of such a filter depends on the spectral density functions, average radar cross sections, and the relative mean Doppler frequencies of each type of clutter. lt is shown that the optimal improvement factor of such a filter is bounded by the weighted average (weighted in accordance with the radar cross sections of the clutter types) of the improvement factor for the individual clutter type. lt is also shown that the improvement factor of such a filter is a function of the relative mean Doppler frequency f0 between the clutter types. As f0 increases, the performance of the MTI system degrades. The worst improvement factor occurs when f0 is equal to half of the radar pulse-repetition frequency (PRF).     

Spectral-domain covariance estimation with a priori knowledge

A knowledge-aided spectral-domain approach to estimating the interference covariance matrix used in space-time adaptive processing (STAP) is proposed. Prior knowledge of the range-Doppler clutter scene is used to identify geographic regions with homogeneous scattering statistics. Then, minimum-variance spectral estimation is used to arrive at a spectral-domain clutter estimate. Finally, space-time steering vectors are used to transform the spectral-domain estimate into a data-domain estimate of the clutter covariance matrix. The proposed technique is compared with ideal performance and to the fast maximum likelihood technique using simulated results. An investigation of the performance degradation that can occur due to various inaccurate knowledge assumptions is also presented     

Clutter and jammer multipath cancellation in airborne adaptiveradar

Airborne surveillance radars must detect and localize targets in diverse interference environments consisting of ground clutter, conventional jamming, and terrain scattered jammer multipath. Multidimensional adaptive filtering techniques have been proposed to adaptively cancel this interference. However, a detailed analysis that includes the effects of multipath nonstationarity has been elusive. This work addresses the nonstationary nature of the jammer multipath and its impact on clutter cancellation and target localization. It is shown that the weight updating needed to track this interference will also modulate sidelobe signals. At the very least, this complicates the localization of targets. At the worst, it also greatly complicates the rejection of clutter. Several techniques for improving cancellation of jammer multipath and clutter are proposed, including 1) weight vector interpolation, extrapolation, and updating; 2) filter architecture, constraint, and beamspace selection; 3) prefilters; 4) 3-D STAP architectures; and 5) multidimensional sidelobe target editing     

Implementing digital terrain data in knowledge-aided space-time adaptive processing

Many practical problems arise when implementing digital terrain data in airborne knowledge-aided (KA) space-time adaptive processing (STAP). This paper addresses these issues and presents solutions with numerical implementations. In particular, using digital land classification data and digital elevation data, techniques are developed for registering these data with radar return signals, correcting for Doppler and spatial misalignments, adjusting for antenna gain, characterizing clutter patches for secondary data selection, and ensuring independent secondary data samples. These techniques are applied to select secondary data for a single-bin post-Doppler STAP algorithm using multi-channel airborne radar measurement (MCARM) program data. Results with the KA approach are compared with those obtained using the standard sliding window method for choosing secondary data. These results illustrate the benefits of using terrain information, a priori data about the radar, and the importance of statistical independence when selecting secondary data for improving STAP performance