Optimal polarimetric detection of radar target in a slowlyfluctuating environment of clutter

It is shown that in a situation where a radar target is distant enough from the radar and is included in a natural or artificial clutter environment in such a manner that the conventional detection methods fail, it is possible to improve the radar detection performance by using appropriate signal processing on two orthogonal polarization states. A CFAR (constant false alarm rate) polarimetric detection system based on the study of the polarization difference between clutter and target is proposed. Since the polarization state of the clutter echoes fluctuates slowly from cell to cell, an autoregressive model can be applied to the components of the polarization vector to predict the detection thresholds needed to follow the polarization state variation. The detection thresholds are determined to maintain a false alarm probability equal to 10^-6. The presence of a target registers as a significant variation of the estimation error of the polarization vector. Results obtained from measurements of simple and canonical targets with artificial clutter are presented, and these results validate the principle of polarimetric detection     

A CFAR Design for a Window Spanning Two Clutter Fields

When the heterogeneous clutter field spanning the spatial sampling sliding window can be modeled as two contiguous homogeneous clutter fields with the statistical parameters of each field unknown and independent from field to field and with the transition point between fields also not known, then the cell-averaging constant false alarm rate (CFAR) performance significantly degrades, yielding target masking effects and loss of false alarm regulation. For the same defined and encountered environment spanning the sliding window, the performance degradation effects are shown to be largely eliminated when a newly developed class of CFAR tests is employed. These tests are designated as heterogeneous clutter estimating CFARs (HCE-CFAR). The test initially involves the combined use of multiple hypothesis testing and maximum likelihood estimation procedures to estimate the statistical parameters of each of the two fields, and the transition point between them, and then makes use of the relevant estimated clutter field parameters to effect the final decision rule. HCE-CFAR designs are presented for both the cases when the contiguous fields have Rayleigh first-order probability distributions, and log-normal probability distribution. However, the focus of the development and the conducted performance evaluation is for the Rayleigh clutter cases.     

Matched subspace CFAR detection of hovering helicopters

A constant false alarm rate (CFAR) strategy for detecting a Gaussian distributed random signal against correlated non-Gaussian clutter is developed. The proposed algorithm is based on Scharf's matched subspace detector (MSD) and has the CFAR property with respect to the clutter amplitude probability density function (apdf), provided that the clutter distribution belongs to the compound-Gaussian family and the clutter covariance matrix is known to within a scale factor. Analytical expressions of false alarm and detection probabilities are derived. An application to the problem of detecting hovering helicopters against vegetated ground clutter is reported     

Two CFAR algorithms for interfering targets and nonhomogeneousclutter

The greatest-of-order statistics estimator (GOOSE) constant false alarm rate (CFAR) and the censored greatest-of (CGO) CFAR are described. Both are designed to accommodate interfering targets in the reference window as well as control false alarms in the presence of clutter boundaries. Both succeed in accomplishing these tasks although the CGO-CFAR is preferred as it has a designed graceful degradation as the number of interfering targets exceeds the number of samples censored. The advantage of the GOOSE-CFAR is a theoretical one in that one can derive analytical results for clutter boundary analysis and thus not have to resort to simulations     

Data quantization effects in CFAR signal detection

In this paper, we investigate data quantization effects in constant false alarm rate (CFAR) signal detection. Exponential distribution for the input data and uniform quantization are assumed for the CFAR detector analysis. Such assumptions are valid in the case of radar for a Swerling I target in Gaussian clutter plus noise and a receiver with analog square-law detection followed by analog-to-digital (A/D) conversion. False alarm and detection probabilities of the cell averaging (CA) and order statistic (OS) CFAR detectors operating on quantized observations are analytically determined. In homogeneous backgrounds with 15 dB clutter power fluctuations, we show analytically that a 12-bit uniform quantizer is sufficient to achieve false alarm rate invariance. Detector performance characteristics in nonhomogeneous backgrounds, due to regions of clutter power transitions and multiple interfering targets, are also presented and detailed comparisons are given     

Adaptive False Alarmrm Regulation in Double Threshold Radar Detection

In practical situations the false alarm probability in double threshold radar detection, sometimes known as binary integration with sliding window detection, is dependent on the nonstationarity and azimuthal correlation of the clutter which is present. Control of the false alarm probability can be achieved, to a certain extent, by the adjustment of the second threshold in the detection process. In this study two adaptive control techniques which are based on the statistical characteristics of the data are compared. Comparing the results for a technique based on first-order statistics with one based on second-order statistics, it is shown that the second-order, or correlation sensitive, technique can give a reduction of 30 to 45 percent in the false alarm probability with no corresponding loss in the detection probability. An interesting aspect of the results is the fact that the effects of the size of the sample area and the bias in the correlation estimator are clearly evident.     

Threshold Control for Automatic Detection in Radar Systems

In automatic detection in radar systems an estimate of background clutter power is used to set the detection threshold. Usually detection cells surrounding the cell under test for the presence of a target are used to estimate the clutter power. In the research reported herein, the target location is taken to be uncertain and thus returns from a target could corrupt this clutter power estimate. It is shown how the threshold should be varied to compensate for the resulting degradation in detection performance. The threshold control procedure is based on a priori information about target location that could be supplied by the radar's tracking system. In addition, a simple procedure for calculating detection and false alarm probabilities for Swerling II target models is presented.     

Distributed CFAR detection in homogeneous and nonhomogeneousbackgrounds

The performance of distributed constant false alarm rate (CFAR) detection with data fusion both in homogeneous and nonhomogeneous Gaussian backgrounds is analyzed. The ordered statistics (OS) CFAR detectors are employed as local detectors. With a Swerling type I target model, in the homogeneous background, the global probability of detection for a given fixed global probability of false alarm is maximized by optimizing both the threshold multipliers and the order numbers of the local OS-CFAR detectors. In the nonhomogeneous background with multiple targets or clutter edges, the performance of the detection system is analyzed and its performance is compared with the performance of the distributed cell-averaging (CA) CFAR detection system     

Multiple hypothesis track confirmation for infrared surveillancesystems

An overall methodology is described for the application of a multiple hypothesis tracking (MHT) algorithm to the infrared (IR) surveillance system problem of establishing tracks on dim targets in a heavy clutter or false alarm background. The authors discuss the manner in which the detection and tracking systems are jointly designed to optimize performance. They present approximate methods that can conveniently be used for preliminary system design and performance prediction. They discuss the use of a detailed Monte Carlo simulation for final system evaluation and present results illustrating the proposed methods and comparing predicted and simulation performance     

Optimal radar threshold determination in Weibull clutter andGaussian noise

A technique is presented for determining the ideal detection threshold when Gaussian noise and Weibull distributed clutter returns are present on a radar receiver and neither is dominant. Quantitative data is presented for several clutter types and false alarm probabilities     

基于杂波对消-自聚焦的多通道SAR-GMTI

对超高频(UHF)波段多通道合成孔径雷达(SAR)动目标检测技术进行研究,解决了长相干积累时间导致动目标在方位向散焦严重的问题。采用分块自聚焦技术对多通道SAR地面移动目标指示(GMTI)系统自适应杂波抑制后的SAR图像进行处理,改善杂波抑制后的SAR图像中动目标的聚焦情况,增强动目标与周围剩余杂波的对比度,进而提高恒虚警率(CFAR)检测的性能。与传统杂波抑制后直接进行CFAR检测方法相比较,该方法降低了检测虚警概率。实测数据处理结果显示动目标的信杂比明显提高,动目标方位向聚焦成功,证明了该方法的有效性。     

Radar modelling to improve CFAR performance in the littoral

Littoral operation of radars poses severe signal processing difficulties due to the highly stressing, inhomogeneous clutter. This report describes an initial investigation into the feasibility of utilising site specific radar modelling to provide a localized estimate of the clutter statistics which can then be used to predict the required threshold to maintain a given false alarm rate. The technique has been applied to littoral clutter recordings obtained from the experimental S-band phase array radar, MESAR2. Results are presented for the technique in comparison with a conventional, non-adaptive, cell averaging CFAR. The paper concludes that significant performance enhancements are possible through the use of this new technique.     

一种机动多目标雷达视频信号模拟器的设计

给出了一种机动多目标雷达视频信号模拟器的软硬件设计方案,它可实时输出所需的多个动目标雷达视频信号,雷达信号的类型、目标的数量、目标的回波特性、目标的运动特性、杂波的类型及参数等均可方便地进行设置。该模拟器可满足各种雷达信号处理算法(如杂波抑制、恒虚警检测、动目标跟踪、多基地数据融合等)的测试与效果分析,以及对雷达信号处理机等进行性能调试与测试的需要。     

Improved CFAR performance in the littoral

Littoral operation of radars poses severe signal processing difficulties due to the highly stressing, inhomogeneous clutter. This report describes an initial investigation into the feasibility of utilising site-specific radar modelling to provide a localised estimate of the clutter statistics which can then be used to predict the required threshold to maintain a given false alarm rate. The technique has been applied to littoral clutter recordings obtained from the experimental S-band phased array radar, MESAR2. Results are presented for the technique in comparison with a conventional, non-adaptive, cell averaging CFAR. This paper concludes that significant performance enhancements are possible through the use of this new technique.     

CFAR for homogeneous part of high-resolution imagery

In high-resolution imaging, weak target pixel amplifiers may not be detected in the presence of clutter containing strong nonhomogeneities, when conventional approaches are used. The authors describe a constant false alarm rate (CFAR) approach that avoids the elimination of these significant target returns. The nonhomogeneous clutter as well as the weak target components are detected with this approach. The targets could then be discriminated from the homogeneities by discrimination techniques. It is shown how the lower amplitude components of the background noise and homogeneous clutter (which have Rayleigh statistics) can be detected in the presence of strong homogeneous clutter and targets. The average level of the homogeneous component is then determined using these lower-amplitude components. This CFAR approach avoids having a CFAR on the strong nonhomogeneities as well as the homogeneous component. The avoidance is what yields the ability to detect weak target pixel amplitudes     

Biparametric linear estimation for CFAR against Weibull clutter

The authors deal with constant false alarm rate (CFAR) procedures against nonstationary clutter, modeled as a Weibull distributed process whose scale parameter α and shape parameter β are both variable. It is shown that conventional CFAR procedures, which compensate only for α, degrade intolerably as β deviates from β=2, namely, as the Rayleigh distributional assumption is violated. A biparametric CFAR procedure is shown to be suited to such situations. The authors introduce a logarithmic transformation to reduce the Weibull probability density function (pdf) to a Gumbel pdf, i.e., to the location-scale type, and then exploit the best linear unbiased estimation (BLUE) of location-scale parameters to adjust the detection threshold. True CFAR is thus achieved when the clutter is locally homogeneous. Resilience against local inhomogeneities can also be conferred since BLUE lends itself to censoring. Through a performance analysis, the influence of various system and distributional parameters is elicited     

New clutter suppression approach for multifunction radar-radarresource management for the clutter suppression

The use of constant-false-alarm-rate (CFAR) techniques to keep false alarm rates at a suitable low level is reviewed. Radar resource management for clutter suppression, a novel approach for multifunction phased-array radar that can more effectively suppress clutter, is proposed. The results of a simulation study, which indicate the maximum permissible P_fa and the improvement of detection performance, demonstrate the effectiveness of resource management, especially when an all-neighbor filter is used     

Clutter Map CFAR Analysis

An analysis of the probability of target detection for a clutter map CFAR using digital exponential filtering has been performed. General performance equations are derived. The probability of detection versus signal-to-noise ratio is plotted for a false alarm probability of 1.E-06 for several weight values. The CFAR loss is plotted for a detection probability of 0.9 and false alarm probabilities of 1.E-06 and 1.E-08.     

CFAR behavior of adaptive detectors: an experimental analysis

We conduct an experimental analysis for assessing the constant false alarm rate (CFAR) behavior of four coherent adaptive radar detectors in the presence of experimentally measured clutter data. To this end we exploit several data files containing both land, lake, and mixed land and sea clutter, collected by two radar systems (the MIT Lincoln Laboratory Phase-One radar and the McMaster IPIX radar) at different polarizations, range resolutions, and frequency bands. The results show that all the receivers, in the presence of real data, don't respect their nominal probability of false alarm (P/sub fa/), namely they exhibit a false alarm rate higher than the value preassigned at the design stage. Nevertheless one of them, the recursive persymmetric adaptive normalized matched filter (RP-ANMF) is very robust, in the sense that it presents an acceptable displacement from the nominal P/sub fa/, in correspondence of all the analyzed scenarios.     

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