L. Verrazzani

Statistical analyses of measured radar ground clutter data

The performance of ground-based surveillance radars strongly depends on the distribution and spectral characteristics of ground clutter. To design signal processing algorithms that exploit the knowledge of clutter characteristics, a preliminary statistical analysis of ground-clutter data is necessary. We report the results of a statistical analysis of X-band ground-clutter data from the MIT Lincoln Laboratory Phase One program. Data non-Gaussianity of the in-phase and quadrature components was revealed, first by means of histogram and moments analysis, and then by means of a Gaussianity test based on cumulants of order higher than the second; to this purpose parametric autoregressive (AR) modeling of the clutter process was developed. The test is computationally attractive and has constant false alarm rate (CFAR). Incoherent analysis has also been carried out by checking the fitting to Rayleigh, Weibull, log-normal, and K-distribution models. Finally, a new modified Kolmogorov-Smirnoff (KS) goodness-of-fit test is proposed; this modified test guarantees good fitting in the distribution tails, which is of fundamental importance for a correct design of CFAR processors.

Performance analysis of two adaptive radar detectors against non-Gaussian real sea clutter data

The performance of two adaptive detection schemes developed in the literature, Kellys generalized likelihood ratio test (GLRT) and the adaptive linear-quadratic (ALQ) detector, are tested on real sea clutter data recorded by the IPIX experimental radar. The results of first- and second-order statistical analyses performed on two data sets are reported. Amplitude analysis has been carried out by checking the fitting to Weibull, log-normal, K, and generalized K models. The results show good agreement between performance prediction based on the generalized K model, with texture strongly correlated among primary and secondary data, and the performance obtained by processing the real sea clutter data.

Estimation of chirp radar signals in compound-Gaussian clutter: a cyclostationary approach

Signal detection of known (within a complex scaling) rank one waveforms in non-Gaussian distributed clutter has received considerable attention. We expand on published solutions to consider the case of rank one waveforms that have some unknown parameters, i.e., signal amplitude, initial phase, Doppler shift, and Doppler rate of change. The contribution of this paper is the derivation and performance analysis of two joint estimators of Doppler shift and Doppler rate-the chirp embedded in correlated compound-Gaussian clutter. One solution is based on the maximum likelihood (ML) principle and the other one on target signal second-order cyclostationarity. The hybrid Cramer-Rao lower bounds (HCRLBs) and a large sample closed-form expression for the mean square estimation error (only for the Doppler shift) are also derived. Numerical examples are provided to show the behavior of the proposed estimator under different non-Gaussian clutter scenarios.

Decentralized CFAR detection with binary integration in Weibull clutter

The analysis of a distributed multiradar system with decentralized decisions, operating in the presence of nonstationary Weibull clutter is presented. Each sensor employs a constant false-alarm rate (CFAR) algorithm and binary integration and transmits its local decisions to the fusion center, which takes the final decision. Optimal local double thresholds and decision fusion rule are determined according to the Neyman-Pearson (N-P) criterion and the global performance is evaluated by a novel approach to optimize distributed binary integration systems.

Multibaseline ATI-SAR for robust ocean surface velocity estimation

An open problem of along-track interferometry (ATI) for synthetic aperture radar (SAR) sensing of ocean surface currents is the need of ancillary wind information for inversion of Doppler centroid measurements, that have to be compensated for the propagation velocity of advancing and/or receding Bragg scatterers. We propose three classes of estimators which exploit multibaseline (MB) ATI acquisition and Doppler resolution for robust data inversion under different degrees of a priori information about the wind direction and the value of the characteristic Bragg frequency. Performance analysis and comparison with conventional ATI show that the proposed MB estimators can produce accurate velocity estimates in the absence of detailed ancillary data.

Cramer-Rao bounds and estimation of the parameters of the Gumbel distribution

Maximum Likelihood (ML) algorithms and Cramer-Rao (CR) bounds for the location and scale parameters of the Gumbel distribution are discussed. First we consider the case in which the scale parameter is known, obtaining the estimator of the location parameter by solving the likelihood equation and then evaluating its performance. We next consider the case where both the location parameter and the scale parameter are unknown and need to be estimated simultaneously from the reference samples. For this case, performance is analyzed by means of Monte Carlo simulation and compared with the asymptotic CR bound. >

Maximum likelihood, ESPRIT, and periodogram frequency estimation of radar signals in K-distributed clutter

Abstract The contribution of this paper is the derivation of the joint maximum likelihood (ML) estimator of complex amplitude and Doppler frequency of a radar target signal embedded in correlated non-Gaussian clutter modelled as a compound-Gaussian process. The estimation accuracy of the ML frequency estimator is investigated and compared with that of the well-known periodogram and ESPRIT estimators under various operational scenarios. The hybrid Cramer–Rao lower bound (HCRLB) and a large sample closed-form expression for the mean square estimation error are also derived for Swerling I target signal. Finally, numerical results obtained by Monte Carlo simulation are checked by means of measured sea clutter data for the general case of fluctuating target amplitude.

Radar detection of targets: new theoretical findings and results by processing recorded live data

An overview on a number of new theoretical findings is presented for the optimum and suboptimum radar detection of fluctuating targets against a composite disturbance, which is modeled as a mixture of coherent K-distributed and Gaussian distributed disturbance. The optimum coherent detector, which derives from the likelihood ratio test, is used as the performance bound. Starting from some of its possible different analytical formulations, the optimum detector is approximated to give a family of suboptimum detectors, with performance close to optimal. The adaptive implementation of these detectors is discussed and the adaptivity loss is evaluated. The detection schemes are then fed with recorded live clutter data, and their performance are analyzed in such environment. The validity of the theoretical analysis is thus assessed as well as the practicality of the suboptimum detection schemes presented.

Coverage area analysis for decentralized detection in Weibull clutter

The performance of a decentralized constant false-alarm rate (CFAR) detection system with data fusion in homogeneous non-Gaussian background is analyzed in terms of ground area covered. The advantages of using a distributed radar system and the differences between the system behavior in Rayleigh clutter and in Weibull clutter are stressed. Notably, the increasing benefit of cooperative decision making when clutter becomes spikier is pointed out.

System and estimation problems for multibaseline InSAR imaging of multiple layovered reflectors

In the recent years there has been growing interest in exploiting the advanced multibaseline operation of synthetic aperture radar interferometry (InSAR) to solve layover effects that can degrade conventional InSAR imagery. In this work we consider detailed modelling of this problem including speckle noise for extended targets, application of several non-parametric and parametric spatial spectral estimation methods to multibaseline layover solution, and system trade-offs analysis. Corresponding statistical accuracy of estimated heights is investigated by simulation.