Luca Pallotta’s research while affiliated with University of Basilicata and other places

Multifunction phased array radars (MPARs) exploit the intrinsic flexibility of their active electronically steered array (ESA) to perform, at the same time, a multitude of operations, such as search, tracking, fire control, classification, and communications. This paper aims at addressing the MPAR resource allocation so as to satisfy the quality of service (QoS) demanded by both line of sight (LOS) and reflective intelligent surfaces (RIS)-aided non line of sight (NLOS) search operations along with communications tasks. To this end, the ranges at which the cumulative detection probability and the channel capacity per bandwidth reach a desired value are introduced as task quality metrics for the search and communication functions, respectively. Then, to quantify the satisfaction level of each task, for each of them a bespoke utility function is defined to map the associated quality metric into the corresponding perceived utility. Hence, assigning different priority weights to each task, the resource allocation problem, in terms of radar power aperture (PAP) specification, is formulated as a constrained optimization problem whose solution optimizes the global radar QoS. Several simulations are conducted in scenarios of practical interest to prove the effectiveness of the approach.

Polarimetric decompositions are used to separate scatterers and identify their physical parameters by analyzing backscattering, coherence, or covariance matrices. Each cell within polarimetric SAR data is seen as a coherent or incoherent combination of different scattering mechanisms. However, targets are not perfectly characterized by these matrices due to the presence of noise components. The main objective of this study is to remedy the latest issue through proper noise effect elimination. Hence, we propose the re-estimation of the coherence matrix, by incorporating a processing phase that searches for the number of elementary scattering mechanisms in each cell. This first step is based on the eigenvalues, which exploit the advantage of polarization basis independent of the eigenvectors. In the second step, a reduced space is defined by the eigenvectors selected, according to the cases of the first step, as those contributing to the construction of the target, excluding those judged to contribute to noise. The characteristic vector and/or the coherence matrix of the average target is then reconstructed in this new space in three different ways: summation of the elementary coherence matrices, applying Bernoulli's probability law, and orthogonal projection on the reduced space. Finally, the Freeman Durden polarimetric decomposition and the H- $\overline{\alpha }$ Wishart classification are used to show the effectiveness of the process in terms of dominant scattering mechanism identification. Their application on simulated data and on fully-polarized RadarSat-2 images of the city of Algiers attests to the performance of the proposed methodology to improve the identification of dominant scattering mechanisms.

The modern battlefield scenario is strongly influenced by the innovative capabilities of the multifunction phased array radars (MPARs) which can perform sequentially or in parallel a plethora of sensing and communication activities. As a matter of fact, the MPAR can functionally cluster its phased array into bespoke sub-apertures implementing different tasks. Accordingly, a portion of the other available resources, e.g., bandwidth, power-aperture product (PAP), and time, is also assigned to each sub-aperture and the grand challenge is the definition of strategies for an optimal scheduling of the tasks to be executed. In this respect, a rule-based algorithm for task scheduling is proposed in this paper. In a nutshell, in each time window, the procedure first allocates the radar tasks (viz. volume search, cued search, update and confirmation tracking) and then utilize the communication (COM) looks so as to fill the empty intra-slot time left by the radar tasks. When there are two concurrent looks, the allocation is performed according to their priorities. Moreover, if the bandwidth and PAP are sufficient, some of them can be also scheduled in parallel. Interesting results in term of bandwidth and time occupancy efficiency are observed from simulations conducted in challenging scenarios comprising also multiple maneuvering targets.

Coregistration is among the most important and challenging tasks when dealing with multiple synthetic aperture radar (SAR) images, especially when they are acquired at different time instants and characterised by low signal to noise power ratio (SNR) that contributes to their coherence reduction. However, even if some technological expedients could be implemented to maintain the same trajectory and to compensate for these inaccuracies during the acquisition campaign, multitemporal SAR images always need additional registration refinements after compression. Usually, to coregister a series of multitemporal SAR images, one of them is selected as the master, and the remainders are separately registered to it. Differently, in this study, a new strategy is developed to jointly coregister a stack of multitemporal SAR images. It is based on the exploitation of the cross‐correlations in turn computed from each couple of cross‐correlations (a.k.a. cross‐cross‐correlations) of the extracted patches. By doing so, the method is capable of exploiting also the respective misregistration information between the slaves during the estimation process. In this respect, this methodology is applied to enhance the registration capabilities of the constrained Least Squares (CLS) optimisation method, which instead does not account for the reciprocal information related to the slaves. Several tests are performed on multitemporal airborne‐measured SAR data. Obtained results show the effectiveness of the proposed algorithm in terms of achieved root mean square error for images affected by respective rotations also in comparison with the CLS counterpart.

The research conducted within the audio signal processing field is increasingly focusing on environmental sound classification. This paper presents a low-complexity Fully Con-volutional Network composed of two parallel branches. These branches are responsible for extracting features from the Cadence Frequency Diagram representation and the Chebychev moments, respectively. By utilizing both domains of machine-and deep-learning, the proposed pipeline takes advantage of the unique characteristics of each. The key strength of this architecture lies in its reduced number of layers and parameters , as well as its ability to efficiently compute the Cadence Frequency Diagram and Chebychev moments. The effectiveness of the proposed pipeline is demonstrated through various tests conducted on two audio datasets, namely UrbanSound8K and ESC-50.

Multifunction phased array radars (MPARs) exploit the intrinsic flexibility of their active electronically steered array (ESA) to perform, at the same time, a multitude of operations, such as search, tracking, fire control, classification, and communications. This paper aims at addressing the MPAR resource allocation so as to satisfy the quality of service (QoS) demanded by both line of sight (LOS) and non line of sight (NLOS) search operations along with communications tasks. To this end, the ranges at which the cumulative detection probability and the channel capacity per bandwidth reach a desired value are introduced as task quality metrics for the search and communication functions, respectively. Then, to quantify the satisfaction level of each task, for each of them a bespoke utility function is defined to map the associated quality metric into the corresponding perceived utility. Hence, assigning different priority weights to each task, the resource allocation problem, in terms of radar power aperture (PAP) specification, is formulated as a constrained optimization problem whose solution optimizes the global radar QoS. Several simulations are conducted in scenarios of practical interest to prove the effectiveness of the approach.

Passive multistatic radar networks localize a non-cooperative target exploiting the bistatic measurements associated with signals emitted by several transmitters of opportunity and acquired by multiple receivers. However, in realistic scenarios, it might happen that one or more receivers are damaged and/or under malicious attacks with a consequent degradation of the system performance. This paper proposes a procedure to reveal sensors that return anomalous measurements due to an attack or a failure. Specifically, we first detect such measurements by solving a binary hypothesis test, then we cancel them and estimate the final delays as well as the target position. The performance of the overall architecture is assessed using both synthetic data and real-recorded data.