基于先验知识及其定量评估的自适应杂波抑制研究

 为了改善训练样本数受限的非均匀杂波环境中的系统检测性能,研究了基于先验知识及其定量评估的自适应杂波抑制算法。提出了使用经真实杂波信息白化后的先验杂波协方差矩阵与单位矩阵之差的谱范数,来定量评估杂波先验知识的准确程度,并给出了真实杂波协方差矩阵未知时的矩阵谱范数估计方法。结合先验知识定量评估结果,获得了具有先验知识约束时的杂波协方差矩阵最大似然估计方法。分别基于多脉冲相参雷达以及空时自适应雷达进行了杂波建模,在此基础之上分析了算法性能。仿真结果证实了该算法优于使用样本协方差矩阵及先验杂波信息形成杂波抑制权值的性能。     

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.     

Space-time adaptive radar performance in heterogeneous clutter

Traditional analysis of space-time adaptive radar generally assumes the ideal condition of statistically independent and identically distributed (IID) secondary data. To the contrary, measured data suggests realistic clutter environments appear heterogeneous and so the secondary data is no longer IID. Heterogeneity leads to mismatch between actual and estimated covariance matrices, thereby magnifying the loss between the adaptive implementation and optimum condition. Concerns regarding the impact of clutter heterogeneity on space-time adaptive processing (STAP) warrant further study. To this end, we propose space-time models of amplitude and spectral clutter heterogeneity, with operational airborne radar in mind, and then characterize expected STAP performance loss under such heterogeneous scenarios. Simulation results reveal loss in signal-to-interference plus noise ratio (SINR) ranging between a few tenths of a decibel to greater than 16 dB for specific cases     

非高斯杂波下自适应雷达目标检测新方法

在球不变随机向量(SIRV)非高斯杂波背景下,研究了多脉冲相参雷达目标的自适应检测问题。假设杂波具有相同的协方差矩阵结构和可能相关的纹理分量,提出了新的协方差矩阵估计器,并获得了相应的自适应归一化匹配滤波器(ANMF)。理论分析表明,在估计杂波分组大小与实际情况匹配时,所获得的ANMF对杂波功率水平和协方差矩阵结构均具有恒虚警率(CFAR)特性。仿真结果表明:当估计的杂波分组大小失配时,所获得的ANMF具有近似CFAR特性,并进一步分析了不同参数变化对所提检测器性能的影响。与已有的ANMF相比,所获得的ANMF具有更好的检测性能,且迭代次数更小,其相对于已知杂波协方差矩阵的最优归一化匹配滤波器(NMF)的检测损失也更小,具有很好的实际应用前景。     

基于先验门限优化准则的探测阈值自适应选择

针对 2维测量和 4 -sigma确认门 ,把先验检测门限优化准则和修正 Riccati方程的解析近似表示相结合 ,得到了在瑞利起伏环境下使跟踪性能优化的信号探测阈值解析表示式 ,从而使在线求解自适应信号探测阈值能比较容易地实现。通过研究和仿真发现 :在滤波稳定阶段 ,本文给出的自适应信号检测门限方法的跟踪性能优于固定虚警率方法的跟踪性能 ;基于先验检测门限优化准则实现检测 -跟踪的联合优化要求信噪比要大于一定的门限 ,在瑞利起伏环境下 ,对 2维测量和 4 -sigma确认门 ,该门限为 1 .57     

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.     

Adaptive detection schemes in compound-Gaussian clutter

Radar detection of coherent pulse trains embedded in compound-Gaussian disturbance with partially known statistics is discussed. We first give a thorough derivation of two recently proposed adaptive detection structures. Next, we derive a different detection scheme exploiting the assumption that the clutter is wide-sense stationary. Resorting to the theory of circulant matrices, in fact, we demonstrate that the estimation of the structure of the clutter covariance matrix can be reduced to the estimation of its eigenvalues, which in turn can be (efficiently) done via fast Fourier transform codes. After a thorough performance assessment, mostly carried on via computer simulations, the results show that the newly proposed detector achieves better performance than the two previously introduced adaptive detectors. Moreover, a sensitivity analysis shows that, even though this detector does not strictly guarantee the constant false alarm rate property with respect to the clutter covariance matrix, it is robust, in the sense that its performance is only slightly affected by variations in the clutter temporal correlation     

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.     

Further results on adaptive filtering with embedded CFAR

Among the few known adaptive filtering algorithms which have an embedded (integrated) constant false alarm rate (CFAR) performance feature, the generalized likelihood ratio (GLR) test algorithm has been found to be robust in non-Gaussian clutter. This paper examines the detection performance of the GLR algorithm in nonhomogeneous/nonstationary clutter environments which lead to nonidentical distribution of secondary (training) data. For two common types of nonhomogeneity, i.e., the so-called “signal contamination” and “clutter edge”, the asymptotic detection performance is derived and compared with simulations. These asymptotic results are relatively simple to use and they predict the GLR performance in nonhomogeneous environments quite well. The GLR performance loss due to the nonhomogeneity is also evaluated. It is found that the “generalized angle” between the desired and contaminating signal plays an important role in the study of the effects of signal contamination. It is also found that the performance degradation due to the clutter edge depends largely on the width of the clutter spectrum and target-clutter Doppler separation     

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     

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。     

Adaptive Radar Detection in Doubly Nonstationary Autoregressive Doppler Spread Clutter

The problem of adaptive radar detection in clutter which is nonstationary both in slow and fast time is addressed. Nonstationarity within a coherent processing interval (CPI) often precludes target detection because of the masking induced by Doppler spreading of the clutter. Across range bins (i.e., fast time), nonstationarity severely limits the amount of training data available to estimate the noise covariance matrix required for adaptive detection. Such difficult clutter conditions are not uncommon in complex multipath propagation conditions where path lengths can change abruptly in dynamic scenarios. To mitigate nonstationary Doppler spread clutter, an approximation to the generalized likelihood ratio test (GLRT) detector is presented wherein the CPI from the hypothesized target range is used for both clutter estimation and target detection. To overcome the lack of training data, a modified time-varying autoregressive (TVAR) model is assumed for the clutter return. In particular, maximum likelihood (ML) estimates of the TVAR parameters, computed from a single snapshot of data, are used in a GLRT for detecting stationary targets in possibly abruptly nonstationary clutter. The GLRT is compared with three alternative methods including a conceptually simpler ad hoc approach based on extrapolation of quasi-stationary data segments. Detection performance is assessed using simulated targets in both synthetically-generated and real radar clutter. Results suggest the proposed GLRT with TVAR clutter modeling can provide between 5–8 dB improvement in signal-to-clutter plus noise ratio (SCNR) when compared with the conventional methods.     

无需辅助数据的分布式目标自适应检测器

在非高斯背景和没有辅助数据的条件下,研究了高分辨率雷达分布式目标的自适应检测问题.首先采用有序检测理论和协方差矩阵的迭代估计方法粗略估计散射点集合,进一步利用迭代估计方法获得协方差矩阵的近似最大似然估计,提出了无需辅助数据的自适应检测器(ADWSD).ADWSD在非高斯背景下具有近似恒虚警率特性,且检测性能远好于修正的...     

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     

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     

Analysis of some modified ordered statistic CFAR: OSGO and OSSOCFAR

It is necessary for automatic detection radars to be adaptive to variations in background clutter in order to maintain a constant false alarm rate (CFAR). A CFAR based on an ordered statistic technique (OS CFAR) has some advantages over the cell-averaging technique (CA CFAR), especially in clutter edges or multiple target environments; unfortunately the large processing time required by this technique limits its use. The authors present two new OS CFARs that require only ahlf the processing time. One is an ordered statistic greatest of CFAR (OSGO), while the other is an ordered statistic smallest of CFAR (OSSO). The OSGO CFAR has the advantages of the OS CFAR with only a negligible increment to the CFAR loss     

空域数据重排的后多普勒自适应处理方法

传统的后多普勒自适应处理方法,如因子法(FA)和扩展因子法(EFA)虽然能大大降低自适应处理时的运算量和独立同分布样本的需求量,但由于实际中均匀训练样本数目的限制,当天线阵元数进一步增大时,FA和EFA抑制杂波和检测动目标的能力会显著恶化。针对这一问题,提出了一种空域数据重排的后多普勒自适应处理方法。该方法将多普勒滤波后的空域数据重排为一行列数相近的矩阵,空域滤波器权系数也表示成可分离的形式,从而得到一双二次代价函数,利用循环迭代的思想求解权系数。实验表明该方法具有快速收敛,所需训练样本少的优点,尤其在大阵列、小样本条件下该方法抑制杂波的性能明显优于FA和EFA。     

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     

Optimal speckle reduction in polarimetric SAR imagery

Speckle is a major cause of degradation in synthetic aperture radar (SAR) imagery. With the availability of fully polarimetric SAR data, it is possible to use the three complex elements (HH, HV, VV) of the polarimetric scattering matrix to reduce speckle. The optimal method for combining the elements of the scattering matrix to minimize image speckle is derived, and the solution is shown to be a polarimetric whitening filter (PWF). A simulation of spatially correlated, K-distributed, fully polarimetric clutter is then used to compare the PWF with other, suboptimal speckle-reduction methods. Target detection performance of the PWF, span, and single-channel |HH|² detectors is compared with that of the optimal polarimetric detector (OPD). A novel, constant-false-alarm-rate (CFAR) detector (the adaptive PWF) is as a simple alternative to the OPD for detecting targets in clutter. This algorithm estimates the polarization covariance of the clutter, uses the covariance to construct the minimum-speckle image, and then tests for the presence of a target. An exact theoretical analysis of the adaptive PWF is presented; the algorithm is shown to have detection performance comparable with that of the OPD