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.     

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     

Multiband coherent radar detection against compound-Gaussianclutter

A Multiband GLRT-LQ (Generalized Likelihood Ratio Test-Linear Quadratic), MBGLRT-LQ, detector is derived for the coherent radar target detection against a compound-Gaussian clutter background. This scheme is an extension to the multiband case of the Asymptotically Optimum Detector (AOD), also derived under the name of GLRT-LQ in. The proposed multiband version of the algorithm shows two main advantages with respect to the original single-band algorithm. 1) For the adaptive implementation, it requires a much smaller area of homogeneous clutter echoes to estimate the covariance matrix of the interference; 2) it provides an optimum processing of the radar echoes when the radar operates in frequency agility, as electronic counter-countermeasure (ECCM) strategy. A closed form performance analysis is provided for the MBGLRT-LQ detector, which is used to compare it with the single-band version. An application to live recorded data is also presented to validate the obtained results     

Adaptive polarimetric target detection with coherent radar. I.Detection against Gaussian background

The derivation of a completely adaptive polarimetric coherent scheme to detect a radar target against a Gaussian background is presented. A previously proposed Generalized Likelihood Ratio Test (GLRT) polarimetric detector is extended to the case of a general number of channels; this exploits the polarimetric characteristics of the received radar echoes to improve the detection performance. Together with the fully adaptive scheme, a model-based detector is derived that has a lower estimation loss. A complete theoretical expression is derived for the detection performance of both proposed polarimetric detectors. They are shown to have Constant False Alarm Rate (CFAR) when operating against Gaussian clutter, but to be sensitive to deviations from the Gaussian statistic. The application to recorded radar data demonstrates the performance improvement achievable in practice     

Structures for radar detection in compound Gaussian clutter

The problem of coherent radar target detection in a background of non-Gaussian clutter modeled by a compound Gaussian distribution is studied here. We show how the likelihood ratio may be recast into an estimator-correlator form that shows that an essential feature of the optimal detector is to compute an optimum estimate of the reciprocal of the unknown random local power level. We then proceed to show that the optimal detector may be recast into yet another form, namely a matched filter compared with a data-dependent threshold. With these reformulations of the optimal detector, the problem of obtaining suboptimal detectors may be systematically studied by either approximating the likelihood ratio directly, utilizing a suboptimal estimate in the estimator-correlator structure or utilizing a suboptimal function to model the data-dependent threshold in the matched filter interpretation. Each of these approaches is studied to obtain suboptimal detectors. The results indicate that for processing small numbers of pulses, a suboptimal detector that utilizes information about the nature of the non-Gaussian clutter can be implemented to obtain quasi-optimal performance. As the number of pulses to be processed increases, a suboptimal detector that does not require information about the specific nature of the non-Gaussian clutter may be implemented to obtain quasi-optimal performance     

Growler detection in sea clutter with coherent radars

Ship navigation through ice-infested waters is a problem of deep concern to the Canadian shipping and exploration industry. Conventional marine radars have difficulty detecting small pieces of glacial ice called growlers which are very hazardous to vessels if struck. In an effort to improve detection performance, X-band radar measurements were collected and analyzed to determine the characteristics of clutter and growler returns that could lead to their separability. These analyses suggested that coherent medium dwell-time processing (i.e., integration times of a fraction of a second) could provide improvement In growler detectability over conventional methods; and long dwell-time processing (i.e., integration times on the order of seconds) could provide even further improvement. We report on the performance of two new coherent, medium dwell-time detectors. A third detector which is representative of conventional marine radar serves as a basis for comparison Although significant improvement in growler detectability is achieved, the two coherent detectors still fall short of operational requirements. This leads to the development of a long dwell-time detector which provides considerably better performance. Empirical results indicate that this new detector could well satisfy stringent operational requirements     

Pade approximations for detectability in K-clutter and noise

The Pade approximation (PA) method is used to analyze the detection performance of single and multiple pulse radar systems operating in K-distributed clutter and thermal noise. Simple approximations for false-alarm and detection probabilities are obtained, using lower order moments for the detection decision statistic. Both envelope and squaring detector laws are considered, with noncoherent integration, for independent and correlated K clutter. The target is assumed to be pulse-to-pulse Rayleigh fading. The methods are a substantial application of the PA methods we have previously published     

Vector subspace detection in compound-Gaussian clutter. Part I: survey and new results

Deals with the problem of detecting subspace random signals against correlated non-Gaussian clutter exploiting different degrees of knowledge on target and clutter statistical characteristics. The clutter process is modeled by the compound-Gaussian distribution. In the first part of the paper, the optimum Neyman-Pearson (NP) detector, the generalized likelihood ratio test (GLRT), and a constant false-alarm rate (CFAR) detector are sequentially derived both for the Gaussian and the compound-Gaussian scenarios. Different interpretations of the various detectors are provided to highlight the relationships and the differences among them. In particular, we show how the GLRT detector may be recast into an estimator-correlator form and into another form, namely a generalized whitening-matched filter (GWMF), which is the GLRT detector against Gaussian disturbance, compared with a data-dependent threshold. In the second part of this paper, the proposed detectors are tested against both simulated data and measured high resolution sea clutter data to investigate the dependence of their performance on the various clutter and signal parameters.     

Covariance matrix estimation for adaptive CFAR detection incompound-Gaussian clutter

We address the estimation of the structure of the covariance matrix and its application to adaptive radar detection of coherent pulse trains in clutter-dominated disturbance modeled as a compound-Gaussian process. For estimation purposes we resort to range cells in spatial proximity with that under test and assume that these cells, free of signal components, can be clustered into groups of data with one and the same value of the texture. We prove that, plugging the proposed estimator of the structure of the covariance matrix into a previously derived detector, based upon the generalized likelihood ratio test (GLRT), leads to an adaptive detector which ensures the constant false alarm rate (CFAR) property with respect to the clutter covariance matrix as well as the statistics of the texture. Finally, we show that this adaptive receiver has an acceptable loss with respect to its nonadaptive counterpart in cases of relevant interest for radar applications     

Studies of target detection algorithms that use polarimetric radardata

Algorithms are described which make use of polarimetric radar information in the detection and discrimination of targets in a ground clutter background. The optimal polarimetric detector (OPD) is derived. This algorithm processes the complete polarization scattering matrix (PSM) and provides the best possible detection performance from polarimetric radar data. Also derived is the best linear polarimetric detector, the polarimetric matched filter (PMF), and the structure of this detector is related to simple polarimetric target types. New polarimetric target and clutter models are described and used to predict the performance of the OPD and the PME. The performance of these algorithms is compared with that of simpler detectors that use only amplitude information to detect targets. The ability to discriminate between target types by exploring differences in polarimetric properties is discussed     

Coherent detection of radar targets in a non-gaussian background

The problem of detecting radar targets against a background of coherent, correlated, non-Gaussian clutter is studied with a two-step procedure. In the first step, the structure of the amplitude and the multivariate probability density functions (pdfs) describing the statistical properties of the clutter is derived. The starting point for this derivation is the basic scattering problem, and the statistics are obtained from an extension of the central limit theorem (CLT). This extension leads to modeling the clutter amplitude statistics by a mixture of Rayleigh distributions. The end product of the first step is a multidimensional pdf in the form of a Gaussian mixture, which is then used in step 2. The aim of step 2 is to derive both the optimal and a suboptimal detection structure for detecting radar targets in this type of clutter. Some performance results for the new detection processor are also given     

Vector subspace detection in compound-Gaussian clutter. Part II: performance analysis

For pt. I see ibid., vol. 38, no. 4, p. 1295 (2002). In this second part we deal with the problem of detecting subspace random signals against correlated non-Gaussian clutter modeled by the compound-Gaussian distribution. In the first part of the paper, we derived the optimum Neyman-Pearson (NP) detector, the generalized likelihood ratio test (GLRT), and a constant false-alarm rate (CFAR) detector; we also provided some interesting interpretations of them. In this second part, these detectors are tested against both simulated data and measured high resolution sea clutter data to investigate the dependence of their performance on the various clutter and signal parameters. Numerical examples concern a space-time adaptive processing (STAP) scenario and a ground-based surveillance radar system scenario.     

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 polarimetric target detection with coherent radar. II.Detection against non-Gaussian background

For pt. I see ibid., vol. 37, no. 4, pp. 1194-1206 (2001).This paper presents the derivation of a polarimetric coherent adaptive scheme to detect a radar target against a non-Gaussian background. This completes the results presented in Part I for the Gaussian background. A Texture Free-Generalized Likelihood Ratio Test (TF-GLRT) detector is derived that exploits the polarimetric characteristics of the received radar echoes to improve the detection performance. The proposed polarimetric detector is shown to have Constant False Alarm Rate (CFAR) when operating against compound-Gaussian clutter with unknown parameters. Its performance is fully characterized by both theoretical analysis and simulation. Moreover, the application to recorded radar data demonstrates the performance improvement achievable in practice     

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.     

Asymptotically optimum radar detection in compound-Gaussian clutter

An asymptotically optimum receiver designed for detecting coherent pulse trains in compound-Gaussian clutter is introduced and assessed. The proposed receiver assumes knowledge of the structure of the clutter covariance matrix, but does not require that of its amplitude probability density function (apdf). Performance is analytically evaluated, showing that the loss, as measured with respect to the corresponding optimum structure, is kept within a few dBs even for a relatively small number of integrated pulses and that it largely outperforms the matched-filter detector under all instances of practical interest. Interestingly, the proposed detector achieves constant false alarm rate (CFAR), regardless of the clutter envelope distribution and, consequently, its power     

Waveform Design for Multistatic Radar Detection

We derive the optimal Neyman-Pearson (NP) detector and its performance, and then present a methodology for the design of the transmit signal for a multistatic radar receiver. The detector assumes a Swerling I extended target model as well as signal-dependent noise, i.e., clutter. It is shown that the NP detection performance does not immediately lead to an obvious signal design criterion so that as an alternative, a divergence criterion is proposed for signal design. A simple method for maximizing the divergence, termed the maximum marginal allocation algorithm, is presented and is guaranteed to find the global maximum. The overall approach is a generalization of previous work that determined the optimal detector and transmit signal for a monostatic radar.     

High Power Radar Implementation of Coherent Waveforms

The radar use of coherent burst waveforms to obtain clutter suppression is summarized and problems arising from the high power implementation of such waveforms are discussed. These problems arise from the nonlinear nature of the typical high power radar transmitter and result in loss of subpulse-to-subpulse amplitude and phase accuracies, causing clutter suppression degradation. adaptive control loop used to measure transmission errors and provide continuous updating to minimize such errors is proposed. Residual transmission errors resulting via use of the control loop are calculated and shown to have an insignificant effect upon the clutter suppression properties of the coherent waveform. Experimental verification of control loop performance is presented.