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This paper proposes a convolution neural network (CNN) architecture for automatic recognition of signals on the basis of their modulations for Cognitive software defined radio (SDR) applications. It is developed starting from two CNNs specifically designed for this problem and is characterized by having a low number of convolution and fully connected layers sharing also a very low number of filters/units. Moreover, Batch normalization is used to increase learning rate and reduce training time. The reduced complexity together with its low operating time make it compliant with real-time SDR applications. The proposed architecture is validated on the RadioML2016.10a dataset showing interesting results in discriminating both analog and digital modulations under different signal to noise ratio (SNR) regimes.

This paper describes a new framework that, exploiting the Kullback-Leibler Divergence, allows to address the design of one-stage adaptive detectors for multiple hypothesis testing problems. Precisely, at the design stage, the problem is formulated in terms of multiple alternative hypotheses competing with the null hypothesis. Then, a one-stage decision scheme is derived in the context of both known model and unknown parameters as well as for the most general case of unknown model and parameters. Interestingly, the resulting detectors are given by the sum of the compressed log-likelihood ratio based on the available data and a penalty term depending on the number of unknown parameters. This general architecture is then particularized to the problem of subspace target detection, and its effectiveness is assessed through simulations also in comparison with its counterpart based on the two-stage paradigm.

Magnetic resonance (MR) images can play a very important role to evaluate patients’ diagnosis. In particular, there is an increasing interest in image processing and advanced texture analysis methods able to extract features from MR images that are not easily to percept by the human eye. Among many, Haralick’s features have been strongly exploited referring to texture analysis of medical images. Therefore, in this paper, we have investigated Haralick’s features computed from MR T2-weighted acquisitions in order to differentiate benign to malignant salivary gland tumors. The study has involved a total of 6 patients affected by salivary gland cancer: from the followup exams performed by radiologists, 3 patients have been identified as benign tumor affected while 3 patients as malignant one. Haralick’s textural features are computed from normalized gray level co-occurrence matrix (GLCM) considering four different spatial relationships. In this preliminary study all the 14 Haralick’s textural features are investigated in our attempt to differentiate benign from malignant salivary gland tumors: the obtained results reveal that these textural features may be useful to point out the differences between the tumor’s nature, helping the clinicians with the diagnosis routine of the disease.

A new class of disturbance covariance matrix estimators for radar signal processing applications is introduced following a geometric paradigm. Each estimator is associated with a given unitary invariant norm and performs the sample covariance matrix projection into a specific set of structured covariance matrices. Regardless of the considered norm, it is shown that the new class of distribution-free estimators shares a shrinkage-type form; besides, the eigenvalues estimate just requires the solution of a one-dimensional convex problem whose objective function depends on the considered unitary norm. At the analysis stage, the effectiveness of the new estimators is assessed in terms of achievable Signal to Interference plus Noise Ratio (SINR) also in comparison with some existing counterparts. © 2017 IEEE.

This paper deals with detection from multipolarization SAR images. The problem is cast in terms of a composite hypothesis test aimed at discriminating between the Polarimetric Covariance Matrix (PCM) equality (absence of target in the tested region) and the situation where the region under test exhibits a PCM with at least an ordered eigenvalue smaller than that of a reference covariance. This last setup reflects the physical condition where the back scattering associated with the target leads to a signal, in some eigen-directions, weaker than the one gathered from a reference area where it is apriori known the absence of targets. A Multi-family Generalized Likelihood Ratio Test (MGLRT) approach is pursued to come up with an adaptive detector ensuring the Constant False Alarm Rate (CFAR) property. At the analysis stage, the behaviour of the new architecture is investigated in comparison with a benchmark (but non-implementable) and some other adaptive sub-optimum detectors available in open literature. The study, conducted in the presence of both simulated and real data, confirms the practical effectiveness of the new approach. © 2016 IEEE.

The challenge of ballistic missiles classification is getting greater importance in last years. In fact, since the antimissile defence systems have generally a limited number of interceptors, it is important to distinguish between warheads and confusing objects that the missile releases during its flight, in order to maximize the interception success ratio. For this aim, a novel micro-Doppler based classification technique is presented in this paper characterized by the employment of Krawtchouk moments. Since the evaluation of the latter requires a low computational time, the proposed approach is suitable for real time applications. Finally, a comparison with the 2-dimensional Gabor filter based approach is described by testing both the techniques on real radar data. © 2016 IEEE.

We deal with performance prediction of the incoherent radar receiver (i.e. squared-law plus integrator) in the presence of independent non-identically distributed (non-id) Weibull target echoes. First of all, we provide the probability density function (pdf) for the sum of independent but non-id Weibull random variables in terms of an infinite sum of Gamma pdfs. Then, we develop an analytic expression for the detection probability as a fast converging series of functions. Finally, we evaluate the impacts on the detection performance of non-id Weibull target parameters. © 2014 IEEE.

In the modern battlefield scenario multiple sources of information may be exploited to mitigate uncertainty. Polarization and spatial diversity can provide useful information for specific and critical tasks such as the Automatic Target Recognition (ATR). In this paper the use of pseudo-Zernike moments applied to the full-polarimetric Gotcha dataset is presented. Specifically improved single platform ATR performance is demonstrated through the use of multiple observations.