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。     

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     

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.     

Improved clutter mitigation performance using knowledge-aided space-time adaptive processing

This paper presents a framework for incorporating knowledge sources directly in the space-time beamformer of airborne adaptive radars. The algorithm derivation follows the usual linearly-constrained minimum-variance (LCMV) space-time beamformer with additional constraints based on a model of the clutter covariance matrix that is computed using available knowledge about the operating environment. This technique has the desirable property of reducing sample support requirements by "blending" the information contained in the observed radar data and the a priori knowledge sources. Applications of the technique to both full degree of freedom (DoF) and reduced DoF beamformer algorithms are considered. The performance of the knowledge-aided beam forming techniques are demonstrated using high-fidelity simulated X-band radar data     

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     

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     

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     

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.     

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。     

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     

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.     

A Nonadaptive Processing of Radar Pulse Trains for Clutter Rejection

The problem of detecting coherent pulse trains with uniform amplitude in a clutter-plus-noise environment is considered. A radar processor for detecting targets moving radially with respect to the clutter is proposed. The minimum interpulse spacing of the transmitted signal is assumed long enough that returns are not received simultaneously from different ranges within a region of extended clutter, and the central frequency of the clutter power spectrum is postulated to be known. The processor is singled out as the linear filter, orthogonal to the clutter central frequency component, which yields the maximum ratio of peak signal power to average noise power. The filter can be implemented by slightly modifying the structure of the conventional matched filter. The performance of such a filter is compared with that achievable if full a priori knowledge of the input interference were available and with that of the conventional matched filter. This comparison is made on a signal-to-interference power ratio basis after assuming a transmitted signal consisting of equally spaced pulses and an interference characterized by an exponential covariance matrix.     

Performance bounds for hot and cold clutter mitigation

A new method is presented for describing the theoretical interference space-time covariance matrix that will be observed in an adaptive airborne radar system under specific topographical conditions. Both hot clutter that is induced by interfering sources and cold clutter that results from the radar transmitter are considered. This method incorporates phenomenology observed under site specific conditions as well as system effects such as array geometry, receiver filtering, and system bandwidth. Use of this formulation rather than sample data analyses that are generally employed enables one to infer performance bounds for site-specific, and thus generally, heterogeneous terrain that are tighter and therefore more meaningful than the thermal noise floor limit     

一种基于协方差矩阵加权的阵元误差补偿STAP处理

机载多通道阵列雷达天线在工程实践中不可避免地存在各类阵元误差,所产生的通道失配问题会对空时二维自适应处理的性能造成大的影响。对存在阵元误差时的阵列信号模型进行了分析,提出了一种基于协方差矩阵加权（CMT）的阵元误差补偿空时自适应处理（STAP）方法,在工程应用中该加权矩阵可通过地面天线定标及校飞过程确定,通过对总干扰协方差矩阵估计的加权预处理,可将实际阵元误差对STAP性能的影响控制在测量误差的影响范围,最后通过仿真验证了算法的有效性。     

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     

一种基于角度-多普勒补偿的均匀圆形天线机载雷达杂波抑制方法

采用均匀圆形相控阵天线的机载雷达杂波分布随距离变化而变化,各距离单元的杂波不再满足独立同分布的条件,造成统计型空时自适应处理(STAP)器性能下降。基于此,本文建立了均匀圆形天线机载雷达模型,对其杂波分布进行了分析,得出了空间角随阵元数非线性变化的特性造成其杂波距离维分布非均匀的结论。研究了一种均匀圆形天线机载雷达杂波抑制方法,该方法先通过修正的角度 多普勒补偿(MADC)预处理消除在杂波谱中心处的非均匀,再利用基于导数更新(DBU)技术进一步减小在其他方位杂波的非均匀程度。仿真结果表明了该方法的有效性。     

Knowledge-based space-time adaptive processing for airborne earlywarning radar

This paper describes an innovative concept for knowledge-based control of space-time adaptive processing (STAP) for airborne early warning radar. The knowledge-based approach holds potential for significant performance improvements over classical STAP processing in nonhomogeneous environments by taking advantage of a priori knowledge. Under this approach, knowledge-based control is used to direct pre-adaptive filtering, and to carefully select STAP algorithms, parameters, and secondary data cells     

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。