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     

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.     

运动声呐空时自适应处理中的多普勒影响分析

为了将空时自适应处理(STAP)理论更好地应用于水下环境,提高运动声呐对混响的抑制性能,研究了多普勒对运动声呐STAP的影响。具体分析了由多普勒造成的回波脉宽伸缩变换以及目标空时导向向量失配带来的影响。分析结果表明,回波包络脉宽变化对于匹配滤波输出信噪比影响较小,而对空时导向向量修正可以有效提高目标方位估计精度以及混响抑制能力。     

CFAR detection and estimation for STAP radar

The algorithm presented here provides both a constant false-alarm rate (CFAR) detection and a maximum likelihood (ML) Doppler-bearing estimator of a target in a background of unknown Gaussian noise. A target is detected, and its parameters estimated within each range gate by evaluating a statistical test for each Doppler-angle cell and by selecting the cell with maximum output and finally comparing it with a threshold. Its CFAR performance is analyzed by the use of the sample matrix inversion (SMI) method and is evaluated in the cases of a fully adaptive space-time adaptive processing (STAP) and two partially adaptive STAPs. The performances of these criteria show that the probability of detection is a function only of the sample size K used to estimate the covariance matrix and a generalized signal-to-noise ratio. The choice of the number K is a tradeoff between performance and computational complexity. The performance curves demonstrate that the finer the resolution is, the poorer the detection capability. That means that one can trade off the accuracy of ML estimation with the performance of the CFAR detection criterion     

Sensitivity of MIMO STAP Radar with Waveform Diversity

Space-time adaptive processing (STAP) is an effective method adopted in airborne radar to suppress ground clutter. Multiple-input multiple-output (MIMO) radar is a new radar concept and has superiority over conventional radars. Recent proposals have been applying STAP in MIMO configuration to the improvement of the performance of conventional radars. As waveforms transmitted by MIMO radar can be correlated or uncorrelated with each other, this article develops a unified signal model incorporating waveforms for STAP in MIMO radar with waveform diversity. Through this framework, STAP performances are expressed as functions of the waveform covariance matrix (WCM). Then, effects of waveforms can be investigated. The sensitivity, i.e., the maximum range detectable, is shown to be proportional to the maximum eigenvalue of WCM. Both theoretical studies and numerical simulation examples illustrate the waveform effects on the sensitivity of MIMO STAP radar, based on which we can make better trade-off between waveforms to achieve optimal system 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。     

Median cascaded canceller for robust adaptive array processing

A median cascaded canceller (MCC) is introduced as a robust multichannel adaptive array processor. Compared with sample matrix inversion (SMI) methods, it is shown to significantly reduce the deleterious effects of impulsive noise spikes (outliers) on convergence performance of metrics; such as (normalized) output residue power and signal to interference-plus-noise ratio (SINR). For the case of no outliers, the MCC convergence performance remains commensurate with SMI methods for several practical interference scenarios. It is shown that the MCC offers natural protection against desired signal (target) cancellation when weight training data contains strong target components. In addition, results are shown for a high-fidelity, simulated, barrage jamming and nonhomogenous clutter environment. Here the MCC is used in a space-time adaptive processing (STAP) configuration for airborne radar interference mitigation. Results indicate the MCC produces a marked SINR performance improvement over SMI methods.     

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     

Optimal and adaptive reduced-rank STAP

This paper is concerned with issues and techniques associated with the development of both optimal and adaptive (data dependent) reduced-rank signal processing architectures. Adaptive algorithms for 1D beamforming, 2D space-time adaptive processing (STAP), and 3D STAP for joint hot and cold clutter mitigation are surveyed. The following concepts are then introduced for the first time (other than workshop and conference records) and evaluated in a signal-dependent versus signal independent context: (1) the adaptive processing “region-of-convergence” as a function of sample support and rank, (2) a new variant of the cross-spectral metric (CSM) that retains dominant mode estimation in the direct-form processor (DFP) structure, and (3) the robustness of the proposed methods to the subspace “leakage” problem arising in many real-world applications. A comprehensive performance comparison is conducted both analytically and via Monte Carlo simulation which clearly demonstrates the superior theoretical compression performance of signal-dependent rank-reduction, its broader region-of-convergence, and its inherent robustness to subspace leakage     

Angle/Doppler estimation in heavy-tailed clutter backgrounds

This work describes new methods on the modeling of the amplitude statistics of airborne radar clutter by means of alpha-stable distributions. We develop joint target angle and Doppler, maximum likelihood-based estimation techniques from radar measurements retrieved in the presence of impulsive uncorrelated noise modeled as an alpha-stable random process. We derive the Cramer-Rao bounds (CRBs) for the additive Cauchy interference scenario to assess the best case estimation accuracy which can be achieved. In addition, we introduce a new joint spatial- and Doppler-frequency high-resolution estimation technique based on the fractional lower order statistics of the measurements of a radar array. Simulation results demonstrate that the proposed methods can be of interest in the study of space-time adaptive processing (STAP) for airborne pulse Doppler radar arrays operating in impulsive interference environments     

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。     

Multistage partially adaptive STAP CFAR detection algorithm

A new method of partially adaptive constant false-alarm rate (CFAR) detection is introduced. The processor implements a novel sequence of orthogonal subspace projections to decompose the Wiener solution in terms of the cross-correlation observed at each stage. The performance is evaluated using the general framework of space-time adaptive processing (STAP) for the cases of both known and unknown covariance. It is demonstrated that this new approach to partially adaptive STAP outperforms the more complex eigen-analysis approaches using both simulated DARPA Mountain Top data and true pulse-Doppler radar data collected by the MCARM radar     

Importance sampling for characterizing STAP detectors

This paper describes the development of adaptive importance sampling (IS) techniques for estimating false alarm probabilities of detectors that use space-time adaptive processing (STAP) algorithms. Fast simulation using IS methods has been notably successful in the study of conventional constant false alarm rate (CFAR) radar detectors, and in several other applications. The principal objectives here are to examine the viability of using these methods for STAP detectors, develop them into powerful analysis and design algorithms and, in the long term, use them for synthesizing novel detection structures. The adaptive matched filter (AMF) detector has been analyzed successfully using fast simulation. Of two biasing methods considered, one is implemented and shown to yield good results. The important problem of detector threshold determination is also addressed, with matching outcome. As an illustration of the power of these methods, two variants of the square-law AMF detector that are thought to be robust under heterogeneous clutter conditions have also been successfully investigated. These are the envelope-law and geometric-mean STAP detectors. Their CFAR property is established and performance evaluated. It turns out the variants have detection performances better than those of the AMF detector for training data contaminated by interferers. In summary, the work reported here paves the way for development of advanced estimation techniques that can facilitate design of powerful and robust detection algorithms     

Reduced-rank STAP performance analysis

The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank (RR) methods outperform full-rank space-time adaptive processing (STAP) when the space-time covariance matrix is estimated from a data set with limited support. The utility of RR methods is demonstrated by theoretical analysis, simulations and analysis of real data. It is shown that RR processing has two opposite effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio (SNR). A method for evaluating the theoretical conditioned SNR for fixed RR transforms is also presented. It Is shown that while best performance is obtained using data-dependent transforms, the loss incurred by the application of fixed transforms (such as the discrete cosine transform) may be relatively small. The main advantage of fixed transforms is the availability of efficient computational procedures for their implementation. These findings suggest that RR methods could facilitate the development of practical, real-time STAP technology     

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     

Performance of a Square Law Pseudonoise Ranging Time-of-Arrival Estimator

The performance of a square law time-of-arrival (TOA) estimator that has been proposed for use in ASTRO-DABS, part of a possible satellite-based fourth generation air traffic control system is considered. The transmitted message consists of a pulse amplitude modulated (PAM) ranging sequence that, due to transmitter characteristics, is corrupted by an unknown frequency offset. The optimum TOA estimator, for the case of no frequency uncertainty, is first presented, together with a lower bound on the variance of the estimate generated. This is followed by the consideration of a suboptimum TOA estimator for which a high signal-to-noise ratio (SNR) performance analysis is carried out; here, the effects of frequency uncertainty are included. Next, the zero-crossing properties of the derivative of the (suboptimum) estimation statistic are presented and the results used to derive an upper bound to the TOA estimate variance that is valid for all SNR values. This latter result is significant because it displays the system threshold effect and complements performance lower bounds that may be derived via other methods. In addition, the method presented here may be applied to other optimum and suboptimum systems where a discrete set of parameters is to be estimated.     

Reiterative median cascaded canceler for robust adaptive array processing

A new robust adaptive processor based on reiterative application of the median cascaded canceler (MCC) is presented and called the reiterative median cascaded canceler (RMCC). It is shown that the RMCC processor is a robust replacement for the sample matrix inversion (SMI) adaptive processor and for its equivalent implementations. The MCC, though a robust adaptive processor, has a convergence rate that is dependent on the rank of the input interference-plus-noise covariance matrix for a given number of adaptive degrees of freedom (DOF), N. In contrast, the RMCC, using identical training data as the MCC, exhibits the highly desirable combination of: 1) convergence-robustness to outliers/targets in adaptive weight training data, like the MCC, and 2) fast convergence performance that is independent of the input interference-plus-noise covariance matrix, unlike the MCC. For a number of representative examples, the RMCC is shown to converge using ~ 2.8N samples for any interference rank value as compared with ~ 2N samples for the SMI algorithm. However, the SMI algorithm requires considerably more samples to converge in the presence of outliers/targets, whereas the RMCC does not. Both simulated data as well as measured airborne radar data from the multichannel airborne radar measurements (MCARM) space-time adaptive processing (STAP) database are used to illustrate performance improvements over SMI methods.     

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     

Automatic target classification of slow moving ground targets in clutter

A new approach is proposed which will allow air-to-ground target classification of slow moving vehicles in clutter. A wideband space-time adaptive (STAP) filter architecture, based on subbanding, is developed and coupled with a one dimensional template-based minimum mean squared error (MMSE) classifier. The performance of this STAP/ATC (automatic target classification) algorithm is quantified using an extensive simulation. The level of residual clutter afforded by various filter configurations and the associated incremental improvement in ATC performance is quantified, revealing the potential for realizable hardware and software implementations to achieve acceptable ATC performance.     

Instrumental variable adaptive array processing

An instrumental variable (IV) approach is presented for estimating the weights of an adaptive antenna array. Theoretical analysis of the IV method shows that the antenna gain weights are independent of finitely correlated noise, so that unbiased estimation of signal arrival angles is possible. Only matrix inversions are required to compute the weight estimates. In this sense, the IV method provides performance comparable with eigenvector techniques but with lower computational burden. Both minimal and overdetermined IV estimators are derived. The overdetermined estimators give the same theoretical array weights as minimal estimators, but yield more accurate weight estimates in real data situations. Simulation results are presented to compare these IV methods with one another and with conventional matrix inversion weight estimators. In these examples it is seen that IV methods are able to resolve closely spaced interference sources when conventional matrix inversion techniques cannot. It is also shown that overdetermined methods are capable of providing weight estimates with lower variances than those of minimal methods