Author Photo
Massimo Rosamilia, Ph.D.

Researcher at University of Naples Federico II

I received the B.S. (summa cum laude) and M.S. degrees, both in computer engineering, from the University of Salerno, Italy, in 2017 and 2019, respectively, and the Ph.D. degree (cum laude) in information technologies and electrical engineering from the University of Naples Federico II, Naples, Italy, in 2023. From September to November 2021, I was a Visiting Researcher with the Cranfield University, Shrivenham, U.K. From September to December 2022, I was a Visiting Researcher with the University of Luxembourg, Luxembourg. My research interest lies in the field of statistical signal processing, with emphasis on radar signal processing. I ranked second in the Student Contest of the first International Virtual School on Radar Signal Processing, in 2020. I also coauthored the paper winning the SET Panel Best Paper Award (Young Scientist) at the NATO SET-319 Specialists’ Meeting on New Mathematical Frontiers for Multi-Dimensional Radar Systems, in 2023.

I am a Student Member of IEEE (since 2020), a member of the IEEE Signal Processing Society (since 2020), and a member of the National Inter-University Consortium for Telecommunications - CNIT (since 2020). I also serve as a reviewer for the following journals: IEEE Transactions on Signal Processing (since 2020), IEEE Transactions on Aerospace and Electronic Systems (since 2021), IEEE Transactions on Geoscience and Remote Sensing (since 2021), IEEE Transactions on Antennas and Propagation (since 2023), Springer Signal, Image and Video Processing (since 2021), and IEEE Journal on Selected Areas in Communications (2021). In the past, I've been a reviewer for the following conferences: IEEE Radar Conf 2020, IEEE Radar Conf 2021, IEEE Radar Conf 2023.

Research Papers

Journals

Single-Pulse Simultaneous Target Detection and Angle Estimation in a Multichannel Phased Array Radar

Authors: A. Aubry, A. De Maio, S. Marano, and M. Rosamilia

Journal: IEEE Transactions on Signal Processing

Year: 2020

Abstract: This paper is focused on simultaneous target detection and angle estimation with a multichannel phased array radar. Resorting to a linearized expression for the array steering vector around the beam pointing direction, the problem is formulated as a composite binary hypothesis test where the unknowns, under the alternative hypothesis, include the target directional cosines displacements with respect to the array nominal coarse pointing direction. The problem is handled via the Generalized Likelihood Ratio (GLR) criterion (both one-step and two-step) where decision statistics leveraging the Maximum Likelihood Estimates (MLEs) of the parameters are compared with a detection threshold. If crossed, target presence is declared and the MLEs of the aforementioned displacements directly provide target angular position with respect to the pointing direction. From the analytic point of view, ML estimation involves a constrained fractional quadratic optimization problem whose optimal solution can be found via the Dinkelbach's algorithm or approximated through a fast-converging procedure based on a Coordinate Descent (CD) optimization. The performance analysis of the proposed architectures as well as the corresponding discussion is developed in terms of computational complexity, Constant False Alarm Rate (CFAR) behavior, detection performance, and angular estimation accuracy, also in comparison with some counterparts available in the open literature and theoretical benchmark limits.

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Single-Snapshot Angle and Incremental Range Estimation for FDA-MIMO Radar

Authors: L. Lan, M. Rosamilia, A. Aubry, A. De Maio, and G. Liao

Journal: IEEE Transactions on Aerospace and Electronic Systems

Year: 2021

Abstract: This article deals with the problem of angle and incremental range (i.e., the target range offset with respect to the center of the cell under test) estimation with a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar exploiting a single data snapshot as observable. Starting from the observation that the maximum likelihood (ML) estimation entails a 2-D grid search over the parameters of interest, three approximated ML techniques are designed resorting to the coordinate descent algorithm and the adaptive monopulse criterion (employing either real or complex slope/bias corrections). At the analysis stage, the estimation performance of the proposed methods, including the tapered and double-step monopulse versions, is also assessed in comparison with the Cramér–Rao lower bound. Numerical results corroborate the effectiveness of the considered estimation strategies in some diverse simulated scenarios.

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Reconfigurable Intelligent Surfaces for N-LOS Radar Surveillance

Authors: A. Aubry, A. De Maio, and M. Rosamilia

Journal: IEEE Transactions on Vehicular Technology

Year: 2021

Abstract: This paper deals with the use of Reconfigurable Intelligent Surfaces (RISs) for radar surveillance in Non-Line Of Sight (N-LOS) scenarios. First of all, the geometry of the scene and the new system concept is described with emphasis on the required operative modes and the role played by the RIS. Then, the specific radar equation (including the RIS effect) is developed to manage the coverage requirements in the challenging region where the LOS is not present. Both noise and clutter interference cases (pulse length-limited and beamwidth-limited surface clutter as well as volume clutter) are considered. Hence, a digression on the use of the radar timeline for the new operative mode is presented together with the data acquisition procedure and the resolution issues for the range, azimuth, and Doppler domains. Finally, the interplay among the system parameters and, in particular, those involving the RIS is discussed and analyzed via numerical simulations.

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Structured Covariance Matrix Estimation with Missing-(complex) Data for Radar Applications via Expectation-Maximization

Authors: A. Aubry, A. De Maio, S. Marano, and M. Rosamilia

Journal: IEEE Transactions on Signal Processing

Year: 2021

Abstract: Structured covariance matrix estimation in the presence of missing-(complex) data is addressed in this paper with emphasis on radar signal processing applications. After a motivation of the study, the array model is specified and the problem of computing the maximum likelihood estimate of a structured covariance matrix is formulated. A general procedure to optimize the observed-data likelihood function is developed resorting to the expectation-maximization algorithm. The corresponding convergence properties are thoroughly established and the rate of convergence is analyzed. The estimation technique is contextualized for two practically relevant radar problems: beamforming and detection of the number of sources. In the former case an adaptive beamformer leveraging the EM-based estimator is presented; in the latter, detection techniques generalizing the classic Akaike information criterion, minimum description length, and Hannan–Quinn information criterion, are introduced. Numerical results are finally presented to corroborate the theoretical study.

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Adaptive Radar Detection in the Presence of Missing-Data

Authors: A. Aubry, V. Carotenuto, A. De Maio, M. Rosamilia, and S. Marano

Journal: IEEE Transactions on Aerospace and Electronic Systems

Year: 2022

Abstract: This article deals with the problem of adaptive radar detection in a missing-data context, where the complete observations (i.e., downstream information loss mechanisms) are characterized by homogeneous Gaussian disturbance with an unknown but possibly structured covariance matrix. The detection problem, formulated as a composite hypothesis test, is tackled by resorting to suboptimal design strategies, leveraging the generalized likelihood ratio criterion demanding appropriate maximum likelihood estimates (MLEs) of the unknowns under both hypotheses. Capitalizing on some possible a priori knowledge about the interference covariance matrix structure, the optimization problems involved in the MLE computation are handled by employing the expectation–maximization (EM) algorithm or its expectation–conditional maximization and multicycle EM variants. At the analysis stage, the performance of the devised architectures is assessed both via Monte Carlo simulations and on measured data for some covariance matrix structures of practical interest.

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Adaptive Target Detection with Polarimetric FDA-MIMO Radar

Authors: L. Lan, M. Rosamilia, A. Aubry, A. De Maio, G. Liao, and J. Xu

Journal: IEEE Transactions on Aerospace and Electronic Systems

Year: 2023

Abstract: The problem of adaptive radar detection with a polarimetric Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar is addressed in this paper. At the design stage, the target detection problem is formulated as a composite hypothesis test, with the unknowns given by the target angle, incremental range (target displacement with respect to the center of the occupied range cell), and scattering matrix, as well as the interference covariance matrix. The formulated detection problem is handled by resorting to sub-optimal design strategies based on the Generalized Likelihood Ratio (GLR) criterion. The resulting detectors demand, under the H1 hypothesis, the solution of a box-constrained optimization problem for which several iterative techniques, i.e., the Linearized Array Manifold (LAM), the Gradient Projection Method (GPM), and the Coordinate Descent (CD) algorithms, are exploited. At the analysis stage, the performance of the proposed architectures, which ensure the bounded CFAR property, is evaluated via Monte Carlo simulations and compared with the benchmarks in both white and colored disturbance.

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Radar Detection Performance Prediction using Measured UAVs RCS Data

Authors: M. Rosamilia, A. Balleri, A. De Maio, A. Aubry, and V. Carotenuto

Journal: IEEE Transactions on Aerospace and Electronic Systems

Year: Early Access, 2022

Abstract: This paper presents measurements of Radar Cross Section (RCS) of five Unmanned Aerial Vehicles (UAVs), comprising both consumer grade and professional small drones, collected in a semi-controlled environment as a function of azimuth aspect angle, polarization and frequency in the range 8.2-18 GHz. The experimental setup and the data pre-processing, which include coherent background subtraction and range gating procedures, are illustrated in detail. Furthermore, a thorough description of the calibration process, which is based on the substitution method, is discussed. Then, a first-order statistical analysis of the measured RCSs is provided by means of the Cramér-von Mises (CVM) distance and the Kolmogorov-Smirnov (KS) test. Finally, radar detection performance is assessed on both measured and bespoke simulated data (leveraging the results of the developed statistical analysis), including, as benchmark terms, the curves for non-fluctuating and Rayleigh fluctuating targets.

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Adaptive Radar Detection and Bearing Estimation in the Presence of Unknown Mutual Coupling

Authors: A. Aubry, A. De Maio, L. Lan, and M. Rosamilia

Journal: IEEE Transactions on Signal Processing

Year: 2023

Abstract: This paper deals with joint adaptive radar detection and target bearing estimation in the presence of mutual coupling among the array elements. First of all, a suitable model of the signal received by the multichannel radar is developed via a linearization procedure of the Uniform Linear Array (ULA) manifold around the nominal array looking direction together with the use of symmetric Toeplitz structured matrices to represent the mutual coupling effects. Hence, the Generalized Likelihood Ratio Test (GLRT) detector is evaluated under the assumption of homogeneous radar environment. Its computation leverages a specific Minorization-Maximization (MM) framework, with proven convergence properties, to optimize the concentrated likelihood function under the target presence hypothesis. Besides, when the number of active mutual coupling coefficients is unknown, a Multifamily Likelihood Ratio Test (MFLRT) approach is invoked. During the analysis phase, the performance of the new detectors is compared with benchmarks as well as with counterparts available in the open literature which neglect the mutual coupling phenomenon. The results indicate that it is necessary to consider judiciously the coupling effect since the design phase, to guarantee performance levels close to the benchmark.

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Adaptive Target Detection and DOA Estimation with Uniform Rectangular Arrays in the Presence of Unknown Mutual Coupling

Authors: L. Lan, M. Rosamilia, A. Aubry, A. De Maio, and G. Liao

Journal: IEEE Transactions on Radar Systems

Year: 2023

Abstract: This paper investigates joint adaptive target detection and direction of arrival (DOA) estimation via a uniform rectangular array (URA) affected by mutual coupling. Capitalizing on a bespoke linearization of the array manifold and leveraging the banded symmetric Toeplitz block Toeplitz structure for the coupling matrix description, a vectorial model of the useful target echo return is proposed and used to formulate the detection problem. Two decision rules are designed, i.e., the generalized likelihood ratio (GLR) and multifamily likelihood ratio test (MFLRT), with the latter aimed at handling an unknown number of active mutual coupling coefficients. Both demand the joint maximum likelihood (ML) estimates of the coupling coefficients and the target angular displacement parameters which can be obtained solving a non-convex optimization problem. Toward this goal, an iterative procedure based on the minorization-maximization (MM) algorithm is developed. At the analysis stage, the performance of the proposed methods is assessed in terms of detection probability ( Pd ) and DOA root mean square error (RMSE) in comparison with benchmarks and standard strategies that do not account for the mutual coupling phenomenon. The results demonstrate the effectiveness of the proposed approaches to overcome signal mismatches induced by both the DOA uncertainty and mutual coupling.

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Radar Detection Performance Via Frequency Agility Using Measured UAVs RCS Data

Authors: M. Rosamilia, A. Aubry, A. Balleri, V. Carotenuto, and A. De Maio

Journal: IEEE Sensors

Year: 2023

Abstract: This paper addresses radar detection performance prediction (via measured data) for drone targets using a frequency agilitybased incoherent (square-law) detector. To this end, a preliminary statistical analysis of the integrated Radar Cross Section (RCS) resulting from frequency agile pulses is carried out for drones of different sizes and characteristics, using data acquired in a semi-controlled environment for distinct frequencies, angles, and polarizations. The analysis involves fitting the integrated RCS measurements with commonly used one-parametric and two-parametric probability distributions and leverages the Cramér-von Mises distance and the Kolmogorov Smirnov test. Results show that the Gamma distribution appears to accurately model the resulting fluctuations. Hence, the impact of integration and frequency agility on the RCS fluctuation dispersion is studied. Finally, detection performance of the incoherent square-law detector is assessed for different target and radar parameters, using both measured and simulated data drawn from a Gamma distribution whose parameters follow the preliminary RCS statistical analysis. The results highlight a good agreement between simulated and measurement-based curves.

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FDA-MIMO Transmitter and Receiver Optimization

Authors: L. Lan, M. Rosamilia, A. Aubry, A. De Maio, and G. Liao

Journal: IEEE Transactions on Signal Processing

Year: 2024

Abstract: This paper addresses the joint design of the transmit parameters (i.e., radar code/frequency increments) and the receive filter in a Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar system. The operating environment includes clutter, namely signal-dependent interference tied up to the FDA transmitted waveforms and the antenna array features, along with conventional thermal noise. The chosen optimization policy relies on the constrained maximization of the Signal-to-Interference-plus-Noise Ratio (SINR) which for Gaussian interference is tantamount to maximizing the radar detection performance. In this context, a bespoke Minorization-Maximization (MM)-Maximum Block Improvement (MBI) algorithm is proposed to tackle the resulting constrained non-convex optimization problem. The convergence properties of the resulting procedure are rigorously proven, along with a thorough investigation of the computational complexity for its implementation. Finally, numerical results are provided to show the effectiveness of the new technique under diverse clutter scenarios of practical relevance and in comparison with some counterparts.

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