STAP for clutter suppression with sum and difference beams

A unique approach for airborne radar clutter rejection is developed and evaluated. This spatial and temporal adaptive approach employs the sum and difference beams of an antenna, which has significant practical advantages because it can be implemented with no/little change to the front-end electronics of airborne systems where sum and difference beams already exist for other reasons. The low sidelobe implementation of many sum and difference beam systems and the low gain of the difference beam in the direction of the target gives this approach the potential in many radars for a more predictable response pattern. The impact of these factors is shown in an airborne clutter rejection demonstration where the performance of this approach is compared with that of the factored approach (FA) using additional spatial channels and that of conventional pulse-Doppler (PD) processing. Reliable detection of an injected target is only achieved by this approach     

Biparametric CFAR procedures for lognormal clutter

The authors consider procedures for constant false alarm rate in lognormal clutter, accounting for variations of both the scale and a shape parameter of the clutter. Adaptivity to both parameters is obtained through biparametric estimation based on a sliding window surrounding the radar cell under test. Some procedures exploiting best linear unbiased estimation (BLUE) are presented and compared to a previous procedure called Log-t, which uses maximum likelihood estimation (MLE). The comparison is carried on for both a homogeneous clutter environment and for instances of inhomogeneous environment (clutter edges and spurious targets). In the latter instances, some advantages of BLUE procedures which stem from the opportunity of censoring are highlighted     

基于稀疏重建的MIMO-OTH雷达多模杂波抑制算法

针对由多模传播引起的多普勒扩展杂波(SDC)严重影响天波超视距雷达(OTHR)对慢速船舰目标的检测这一问题,在基于多输入多输出体制的新一代天波超视距(MIMO-OTH)雷达系统下,利用空域信息抑制多模SDC。传统处理方法需要目标角度先验信息,而在实际中,该信息往往很难获得。为此,提出一种新的基于稀疏重建的多模杂波抑制算法。该算法将二维的稀疏角度搜索转变为一维的角度搜索,使运算量大大降低,且估计出的收发角(DOA-DOD)自动配对,同时可获得各个传播路径下信号的时间多普勒信息,达到了理想的MIMO-OTH雷达多模杂波抑制效果。仿真结果证明了所提算法的有效性。     

Radar detection in clutter

Clutter is defined as any unwanted radar return. The presence of clutter in a range/Doppler cell complicates the detection of a target return signal in that cell. In order to quantify the effect of clutter on the probability of detection, we must first specify sets of models suitable for representing the clutter and target. The simplest and most common model for clutter is based on the gamma density. We include two additional models, the NCG and NCGG clutter models for low grazing angles. They are motivated by physical arguments, the latter of which can accommodate the well-known phenomenon of speckle. Using one of these models for clutter together with one of several models for targets, we determine, in a range/Doppler cell, expressions for probabilities of detection of a target in the presence of clutter. It is important to control the probability of false alarms. The presence of clutter in a cell necessitates an increase in the detection threshold setting in order to control false alarms, thus lowering the probability of detection. If the clutter level is unknown, then we need to take measurements of the clutter and use it to adjust the threshold. The more clutter samples we take, the better the estimate of the clutter level and the less is the resulting detection loss. Using the expressions for the probability of detection in clutter, we can quantify the detection loss for a pair of commonly used constant false-alarm rate (CFAR) techniques and investigate how the loss varies with different parameter values, especially with regard to the number of clutter samples taken to estimate the clutter level.     

风电场雷达杂波动态重构抑制方法

随着风电场的大范围建设，风轮机杂波对雷达的干扰问题日益严重，常规杂波抑制方法难以有效解决风轮机杂波（WTC）干扰问题，因此提出了一种利用动态字典对风轮机杂波进行稀疏重构进而抑制的方法。首先，建立了WTC干扰下的雷达信号模型并分析了风轮机杂波的信号特征。其次，依据WTC的时频特征提出了一种微动参数粗估计方法，利用粗估计结果缩小了字典稀疏重构参数范围，在此基础上利用正交匹配追踪（OMP）算法对字典进行动态生成，并逐级更新字典原子。最后，通过动态字典对风轮机杂波信号进行稀疏重构，从而实现了对WTC的有效抑制。通过仿真实验，分析了风轮机杂波对目标检测的干扰影响，验证了基于动态稀疏重构的风电场杂波抑制方法在不同情况下的有效性。     

Time-frequency method for detecting an accelerating target in sea clutter

The authors design a time-frequency (TF) method for use in high-frequency surface-wave radar (HFSWR) for detecting a small accelerating target in sea clutter. The clutter is modelled by pseudo targets moving with Bragg velocity towards and away from the radar. The design is based on the Wigner distribution (WD) defined by Chan (type-III WD, in our terminology) rather than the WD defined by Claasen and Mecklenbrauker (1980) (2times type-I WD, in our terminology). Like the type-I WD, the type-III WD also concentrates a chirp signal onto a straight line in the TF plane. The type-III WD has the following advantages: 1) Its range of unambiguously measurable frequencies (RUMF) is [-pi,pi] rad/s, whereas for the type-I WD the RUMF is [-pi/2,pi/2] rad/s. 2) It allows a target separated from the clutter by pi rad/s to be detected, whereas the type-I WD coalesces such a target with the clutter and thereby mask it. An ambiguity function (AF) was defined corresponding to the type-III WD and use it to derive a smoothed type-III WD that mitigates the clutter. The smoothed type-III WD method is applied to real radar data and shown to be superior to the conventional Fourier transform method. The advantages of the type-III WD over the type-I WD are also demonstrated. The design principles laid out in the paper can also be used to develop a TF method for use in air traffic control radar (ATCR) for detecting an accelerating target in land clutter     

A method for estimating parameters of K-distributed clutter

A method for estimating the parameters of K-distributed clutter when the available sample size of the data is limited is proposed. In this method, the arithmetic mean and geometric mean of the given data are used to estimate the model parameters. Expressions characterizing the performance of the proposed estimator are presented, along with some simulation results. For spiky clutter, simulations show that parameter estimates obtained from the arithmetic and geometric mean are approximately equal to the numerically evaluated maximum-likelihood (ML) estimates. The method is also used to estimate the parameter of the Weibull density     

A relative clutter metric

Clutter plays a very important role in the area of machine and human-in-the-loop target acquisition. A great deal of interest has recently been shown in assessing several different definitions of clutter. In spite of so many definitions available, no single clutter definition has been agreed on by the infrared community as being the best. We develop a new clutter metric, called relative clutter, which is based on factor analysis which is extensively used for statistical analysis. This relative clutter combines many definitions of clutter. Different methods for calculating the relative clutter based on the magnitude of the eigenvalues obtained from the correlation matrix are suggested. The relative clutter of many images is analyzed     

Spatially distributed target detection in non-Gaussian clutter

Two detection schemes for the detection of a spatially distributed, Doppler-shifted target in non-Gaussian clutter are developed. The non-Gaussian clutter is modeled as a spherically invariant random vector (SIRV) distribution. For the first detector, called the non-scatterer density dependent generalized likelihood ratio test (NSDD-GLRT), the detector takes the form of a sum of logarithms of identical functions of data from each individual range cell. It is shown under the clutter only hypothesis, that the detection statistic has the chi-square distribution so that the detector threshold is easily calculated for a given probability of false alarm P_F. The detection probability P_D is shown to be only a function of the signal-to-clutter power ratio (S/C)_opt of the matched filter, the number of pulses N, the number of target range resolution cells J, the spikiness of the clutter determined by a parameter of an assumed underlying mixing distribution, and P_F. For representative examples, it is shown that as N, J, or the clutter spikiness increases, detection performance improves. A second detector is developed which incorporates a priori knowledge of the spatial scatterer density. This detector is called the scatterer density dependent GLRT (SDD-GLRT) and is shown for a representative case to improve significantly the detection performance of a sparsely distributed target relative to the performance of the NSDD-GLRT and to be robust for a moderate mismatch of the expected number of scatterers. For both the NSDD-GLRT and SDD-GLRT, the detectors have the constant false-alarm rate (CFAR) property that P_F is independent of the underlying mixing distribution of the clutter, the clutter covariance matrix, and the steering vector of the desired signal     

Generalized radar clutter model

A commonly used density model for radar clutter is chi-square for power, or, equivalently, Rayleigh for amplitude. However, for many modern high resolution radar systems, this density underestimates the tails of the measured clutter density. Log normal and Weibull distributions have proved to be better suited for the clutter in these high resolution radars. Generalizing the chi-square density by replacing it with the noncentral chi-square density and allowing the mean power level (the noncentrality parameter) to vary, we can both suitably shape the clutter density to produce larger tails and model the fluctuation of the average clutter power, commonly referred to as speckle. The resulting form, although appearing cumbersome, readily allows for efficient and accurate computations of the probability of detection in clutter     

Correlated K-distributed clutter generation for radar detection andtrack

The generation of correlated vectors for non-Gaussian clutter is considered for log normal, Weibull, and K-probability distributions. Previous results for log normal and Weibull distributions are summarized. Expressions for the probability distributions and moments of K-distributed clutter of any correlation are derived. Procedures for forming samples of each type of clutter are shown to be equivalent to passing white Gaussian noise through a linear filter followed by a nonlinear operation. Curves of correlation coefficients necessary for the simulation of these vectors are presented for each distribution     

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.     

Nonparametric rank detectors under K-distributed clutter in radar applications

This correspondence deals with a comparative analysis of parametric detectors versus rank ones for radar applications, under K-distributed clutter and nonfluctuating and Swerling II target models. We show that the locally optimum detectors (LODs) (optimum for very low signal-to-clutter ratio (SCR)) under K-distributed clutter are not practical detectors; on the contrary, asymptotically optimum detectors (optimum for high SCR) are the practical ones. The performance analysis of the parametric log-detector and the nonparametric (linear rank) detector is carried out for independent and identically distributed (IID) clutter samples, correlated clutter samples, and nonhomogeneous clutter samples. Some results of Monte Carlo simulations for detection probability (P/sub d/) versus SCR are presented in curves for different detector parameter values.     

Optimal CFAR detection in Weibull clutter

Optimal, in the maximum likelihood sense, constant false-alarm rate (CFAR) detection for Weibull clutter statistics, is investigated. The proposed OW (optimal Weibull) estimator is proved to be an asymptotically efficient estimator of the mean power of the Weibull clutter. Theoretical analysis of the OW-CFAR detector is provided, while detection performance analysis is carried out using the Monte Carlo simulation method. The operation of the median and morphological (MEMO)-CFAR detector in Weibull clutter statistics is also explained. It performs almost optimally in uniform clutter and, simultaneously, it is robust in multitarget situations. The performance of the proposed OW-CFAR detector in uniformal Weibull clutter is used as a yardstick in the analysis of the MEMO cell-averager (CA) and ordered statistic (OS) CFAR detectors. Nonfluctuating and fluctuating (Swerling II) targets are considered in detection analysis. The performance of the detectors is also examined at clutter edges     

Detection probability using relative clutter in infrared images

Clutter plays a very important role in the area of machine and human-in-the-loop target acquisition. A great deal of interest has recently been shown in assessing several different definitions of clutter. In spite of so many definitions available, no single clutter definition has been agreed on by the target acquisition modeling community as being the best. Here we develop a new clutter metric, called relative clutter, based on factor analysis which is extensively used for statistical analysis. This relative clutter combines many definitions of clutter. Different methods for calculating the relative clutter based on the magnitude of the eigenvalues obtained from the correlation matrix are suggested. The relative clutter of many images is analyzed. The relative clutter is used to calculate probability of detection on Night Vision Lab (NVL) Terrain Board Infrared images     

Analysis of CFAR performance in Weibull clutter

Recent interest has focused on order statistic-based (OS-based) algorithms for calculating radar detection thresholds. Previous analyses of these algorithms are extended, to determine closed-form approximations for the signal-to-clutter ratio required to achieve a particular probability of detection in clutter environments whose amplitude statistics are modeled by the Weibull distribution, and where the clutter dominates receiver noise. Performance is evaluated in both homogeneous and inhomogenous clutter. The analysis shows that the OS-based algorithm is quite robust against both interference and clutter edges. A method is suggested for improving performance at clutter inhomogeneities for short-range targets     

Polarization diversity detection in compound-Gaussian clutter

We present the problem of polarization diversity detection in compound-Gaussian clutter with unknown covariance matrix. To this end we assume that a set of secondary data, free of signal components and with the same covariance structure of the cell under test, is available. Due to the lack of a uniformly most powerful (UMP) detector we resort to a design procedure based upon the Rao and the Wald tests. Specifically we first derive the Rao and the Wald tests assuming that the covariance matrix is known, and then we substitute into the derived decision rules a suitable estimate of the clutter covariance. Interestingly, the newly proposed detectors share the constant false alarm rate (CFAR) property with respect to the texture statistical characterization. Moreover simulation results have shown that the Wald test based detector ensures a performance level higher than the Rao test. We have also conducted a further performance analysis, in the presence of real clutter data and in comparison with the previously proposed generalized likelihood ratio test (GLRT) based receivers, which highlights that, in general, the Wald test receiver outperforms its counterparts. Finally, since the newly proposed decision rules as well as the previously designed GLRTs do not ensure the CFAR property with respect to the clutter covariance matrix, we have developed a sensitivity analysis on the probability of false alarm (P/sub fa/), based on simulated clutter with covariance matrix estimated from real radar data. The results have shown that (P/sub fa/) is only slightly affected by variations in the clutter correlation properties and hence the CFARness is substantially achieved.     

Millimeter wave radar with clutter measurements

A portable millimeter wave test radar system, also suitable for battery operation, gives interesting possibilities for clutter recordings at hard-to-reach sites. The designed system covers all common radar frequencies from the Ka- to V-bands and enables spatial detection of targets or clutter elements within an adjustable time gate, whereby spatial clutter profiles of rain can be analyzed. The construction allows full operation with non-scanning antennas as well. This is advantageous when measuring temporal RCS variations of selected targets or surface clutter from snow dunes.     

Nonlinear clutter cancellation and detection using a memory-basedpredictor

In this paper, a nonlinear prediction (NLP) method is proposed as an alternative to the conventional linear prediction (LP) method for clutter cancellation. Because of the nonlinearity and non-Gaussianity of a clutter process, a nonlinear predictor is therefore needed to suppress clutter optimally. A memory-based predictor which uses a table look-up strategy to perform NLP is used in this work. The advantages of the memory-based approach are fast learning, algorithmic simplicity, robustness and suitability for parallel implementation. The memory-based predictor is then used as an adaptive detector for small surface target detection embedded in clutter. The effectiveness of the new method is demonstrated using real sea clutter data, and the results show improvement when compared with the conventional LP techniques