Alfonso Farina

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.

Knowledge-Aided (Potentially Cognitive) Transmit Signal and Receive Filter Design in Signal-Dependent Clutter

We consider the problem of knowledge-aided (possibly cognitive) transmit signal and receive filter design for point-like targets in signal-dependent clutter. We suppose that the radar system has access to a (potentially dynamic) database containing a geographical information system (GIS), which characterizes the terrain to be illuminated, and some a priori electromagnetic reflectivity and spectral clutter models, which allow the raw prediction of the actual scattering environment. Hence, we devise an optimization procedure for the transmit signal and the receive filter which sequentially improves the signal- to-interference-plus-noise ratio (SINR). Each iteration of the algorithm, whose convergence is analytically proved, requires the solution of both a convex and a hidden convex optimization problem. The resulting computational complexity is linear with the number of iterations and polynomial with the receive filter length. At the analysis stage we assess the performance of the proposed technique in the presence of either a homogeneous ground clutter scenario or a heterogeneous mixed land and sea clutter environment.

Space-time-frequency processing of synthetic aperture radar signals

The subject of this work is the detection and high resolution microwave imaging of objects moving on the ground and observed by an airborne radar. The proposed approach is based on a combined space-time and time-frequency processing. The space-time processing makes use of a linear array antenna and exploits the radar motion for filtering the received echoes in order to improve as much as possible the signal-to-disturbance ratio. The signal is then mapped onto the time-frequency domain, by computing its Wigner-Ville distribution, for a further filtering and for estimating its instantaneous frequency, necessary for the formation of a high resolution image of the moving object. >

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.

Knowledge-based radar signal and data processing: a tutorial review

Radar systems are an important component in military operations. In response to increasingly severe threats from military targets with reduced radar cross sections (RCSs), slow-moving and low-flying aircraft hidden in foliage, and in environments with large numbers of targets, knowledge-based (KB) signal and data processing techniques offer the promise of significantly improved performance of all radar systems. Radars under KB control can be deployed to utilize valuable resources such as airspace or runways more effectively and to aid human operators in carrying out their missions. As battlefield scenarios become more complex with increasing numbers of sensors and weapon systems, the challenge will be to use already available information effectively to enhance radar performance, including positioning, waveform selection, and modes of operation. KB processing fills this need and helps meet the challenge.

Selected list of references on radar signal processing

In recent years an abundance of technical papers and books have been written on several topics of radar signal processing. The detection of radar targets against ground and sea clutter is a problem of great interest in the radar community. The fusion of signals from different radar to improve detection performance is another relevant topic of research and application. These are two of several topics that have been extensively described in the technical literature. Needless to say that the consultation of technical references plays a key role in the daily work of researchers and engineers involved in the radar field. We collected almost 700 references in a single document to facilitate our work and the work of other colleagues of the radar community. The collection of references is by no means exhaustive; the period of screening mainly covers the last two decades.

Consensus CPHD Filter for Distributed Multitarget Tracking

The paper addresses distributed multitarget tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The contribution has been to develop a novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filter that provides a fully distributed, scalable and computationally efficient solution to the problem. The effectiveness of the proposed approach is demonstrated via simulation experiments on realistic scenarios.

Code Design to Optimize Radar Detection Performance Under Accuracy and Similarity Constraints

This paper deals with the design of coded waveforms which optimize radar performances in the presence of colored Gaussian disturbance. We focus on the class of linearly coded pulse trains and determine the optimum radar code according to the following criterion: maximization of the detection performance under a control on the region of achievable Doppler estimation accuracies, and imposing a similarity constraint with a prefixed radar code. This last constraint is tantamount to requiring a similarity between the ambiguity functions of the devised waveform and of the pulse train encoded with the prefixed sequence. The resulting optimization problem is nonconvex and quadratic. In order to solve it, we propose a technique (with polynomial computational complexity) based on the relaxation of the original problem into a semidefinite program. Thus, the best code is determined through a rank-one decomposition of an optimal solution of the relaxed problem. At the analysis stage, we assess the performance of the new encoding technique in terms of detection performance, region of achievable Doppler estimation accuracies, and ambiguity function.

Maximum Likelihood Estimation of a Structured Covariance Matrix With a Condition Number Constraint

In this paper, we deal with the problem of estimating the disturbance covariance matrix for radar signal processing applications, when a limited number of training data is present. We determine the maximum likelihood (ML) estimator of the covariance matrix starting from a set of secondary data, assuming a special covariance structure (i.e., the sum of a positive semi-definite matrix plus a term proportional to the identity), and a condition number upper-bound constraint. We show that the formulated constrained optimization problem falls within the class of MAXDET problems and develop an efficient procedure for its solution in closed form. Remarkably, the computational complexity of the algorithm is of the same order as the eigenvalue decomposition of the sample covariance matrix. At the analysis stage, we assess the performance of the proposed algorithm in terms of achievable signal-to-interference-plus-noise ratio (SINR) both for a spatial and a Doppler processing. The results show that interesting SINR improvements, with respect to some existing covariance matrix estimation techniques, can be achieved.

Radar waveform design in a spectrally crowded environment via nonconvex quadratic optimization

Radar signal design in a spectrally crowded environment is a very challenging and topical problem due to the increasing demand for both military surveillance/remote-sensing capabilities and civilian wireless services. This paper deals with the synthesis of optimized radar waveforms ensuring spectral compatibility with the overlayed licensed electromagnetic radiators. A priori information, for instance, provided by a radio environmental map (REM), is exploited to force a spectral constraint on the radar waveform, which is thus the result of a constrained optimization process aimed at improving some radar performances (such as detection, sidelobes, resolution, tracking). The feasibility of the waveform optimization problem is extensively studied, and a solution technique leading to an optimal waveform is proposed. The procedure requires the relaxation of the original problem into a convex optimization problem and involves a polynomial computational complexity. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable signal to interference plus noise ratio (SINR), spectral shape, and the resulting autocorrelation function (ACF).