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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.

This paper deals with Radar Cross Section (RCS) measurements of five small Unmanned Aerial Vehicles (UAVs) 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, range gating procedures, and calibration, are presented. Then, a thorough statistical analysis of the measured RCSs is provided by means of the Cram´er–von Mises (CVM) distance and the Kolmogorov–Smirnov (KS) test.

International audience; Covariance estimation for the Iterative Closest Point (ICP) point cloud registration algorithm is essential for state estimation and sensor fusion purposes. We argue that a major source of error for ICP is in the input data itself, from the sensor noise to the scene geometry. Benefiting from recent developments in deep learning for point clouds, we propose a data-driven approach to learn an error model for ICP. We estimate covariances modeling data-dependent heteroscedastic aleatoric uncertainty, and epistemic uncertainty using a variational Bayesian approach. The system evaluation is performed on LiDAR odometry on different datasets, highlighting good results in comparison to the state of the art.

This paper deals with the analysis of simultaneously collected co- and cross-polarized bistatic sea-clutter returns with special emphasis on their representation as a Spherically Invari-ant Random Process (SIRP). The study is conducted by using appropriate testing procedures involving the complex envelope of the measured data that provide both first- and higher-order compatibility conditions. The results highlight that the SIRP model is a good candidate for the representation of bistatic coherent clutter, and usually the coherence time of the SIRP texture is longer than that in the monostatic case.

This paper considers the problem of censoring outliers from the secondary dataset in a radar scenario where the sample support is limited. To this end, the generalized regularized likelihood function (GRLF) criterion is used and the corresponding regularized maximum likelihood (RML) estimate of the outlier subset is derived. Since the exact RML estimate involves the solution of a combinatorial optimization problem, a reduced complexity but approximate RML (ARML) procedure is also designed. As to the selection of the regularization parameter, both the expected likelihood (EL) principle and the cross-validation (CV) technique are exploited. At the analysis stage, the performance of the RML and ARML procedure is evaluated based on simulated data in comparison with some previously proposed methods. The results highlight that the RML and ARML algorithm achieves, in general, a satisfactory performance level whereas the previously proposed techniques often experience some performance degradation when the volume of training data is dramatically limited.

This paper deals with the effects of Power Amplifiers (PAs) on radar waveforms designed to ensure spectral compatibility with other Radio Frequency (RF) wireless systems cohabitating in the same frequency band. To this end, a specific hardware-in-the-loop test-bed composed of a Personal Computer (PC), an Arbitrary Waveform Generator (AWG), a nonlinear PA, and a Signal Analyser (SA) is designed. It allows to gather measurements of the amplified signal and to grasp unwanted modifications of its spectral features due to the PA nonlinear distortion. Besides, it permits a quantitative assessment of the discrepancies between the nominal and the actual interference power injected by the radar in the shared frequency bandwidths. To alleviate the spectral distortions induced by the PA and improve spectral coexistence, the use of a Digital Pre-Distortion (DPD) stage upstream the PA is investigated. Modeling techniques together with parameters inference are discussed and applied to the measured waveforms. The obtained experimental results highlight that pairing a suitable waveform design strategy with a DPD processing represents a viable means to mitigate the PA distortions and to realize spectral coexistence.

A new algorithm for 3D localization in multiplat-form radar networks, comprising one transmitter and multiple receivers, is proposed. To take advantage of the monostatic sensor radiation pattern features, ad-hoc constraints are imposed in the target localization process. Therefore, the localization problem is formulated as a non-convex constrained Least Square (LS) optimization problem which is globally solved in a quasi-closed-form leveraging Karush-Kuhn-Tucker (KKT) conditions. The results corroborate the effectiveness of the new strategy which is capable of ensuring a lower Root Mean Square Error (RMSE) than counterpart methodologies, especially in the low Signal to Noise Ratio (SNR) regime.

Unmanned Aerial Vehicles (UAVs) have become widely used in civilian and military applications. There is a recognised need for UAVs surveillance against their malicious use. In this context, radar has emerged as a powerful tool in detecting and tracking UAVs. However, the task is quite complicate due to the low-altitude, low velocity and low radar cross section of small UAVs. In this paper, a smart radar signal processing solution in the presence of multiple unresolved UAVs is devised and applied to UAVs surveillance, which fully capitalizes on the coherent integration, constant false alarm rate detection, target multiplicity test and sparse methods to achieve low computational complexity and high angular resolution. At the analysis stage, numerical simulations are carried out to highlight the capability of proposed solution to detect UAVs and resolve them when they are closely spaced.

Considering a SpaceBorne Radar (SBR) for Space Situational Awareness (SSA), the Fading Occurrence Probability (FOP) is computed for fluctuating targets in weak plasma turbulence as a function of the scintillation index $S_{4}$ . While previous works considered a Rayleigh model for both target fluctuation and plasma scintillation, this paper extends the FOP via the Cumulative Distribution Function (CDF) of the echo power modulated by either non-fluctuating, Rayleigh, and Rice target fluctuations along with Rice plasma scintillation. Downstream physical insights on both channel and target phenomenology leading to such statistical models, numerical results corroborated by Monte Carlo (MC) simulations show that a larger value of the scintillation index $S_{4}$ increases the FOP on echoes. In addition, the aforementioned simulation curves provide a conceptual framework to empirically estimate the scintillation index $S_{4}$ in a monostatic radar configuration.

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. Hence, a digression on the use of the radar timeline for the new operative mode is presented together with a discussion on 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.

The problem of missing sensor measurements can emerge in a variety of radar signal processing applications as for instance beamforming, direction of arrival estimation, interference cancellation, and target detection. The mentioned applications rely on reliable data covariance matrix estimates and suitable procedures, based on the expectation-maximization (EM) algorithm, have been proposed in the open literature to cope with the lack of some entries within specific spatial snapshots. In this paper, the effectiveness of a recent structured covariance matrix estimator [1], accounting for missing data and leveraging possible structural knowledge, is assessed on measured data. Specifically, the estimation procedure is framed in the context of two practically relevant radar applications: beamforming and detection of the number of sources. At the analysis stage, results highlight the effectiveness of the procedure to tackle missing data in the considered radar scenarios.

A new algorithm for Passive Bistatic Radar localization is proposed capitalizing measurements gathered by a colocated active radar and exploiting multiple illuminators of opportunity. Prior information, related to the Passive Bistatic Radar (PBR) receive antenna main-beam size and to the uncertainty characterizing active radar data, is accounted for formalizing ad-hoc constraints for the localization process. Hence, the estimation task is cast as an elliptic positioning problem, according to the constrained Least Squares (LS) framework. The performance of the proposed estimator exhibits sensible improvement with respect to some counterparts.

A hierarchical clustering architecture is proposed to deal with the problem of jamming environment classification when multiple noise-like jammers are possibly present. Assuming the availability of clutter free multichannel data, a two-level hierarchical procedure is devised to unveil the presence of clusters containing range cells experiencing the same jamming interference as the cell under test. Level 1 relies on the use of covariance smoothing and Model Order Selection (MOS) rules to make inference on the number of jamming signals affecting each range bin within the radar range swath. Level 2 allows to discriminate among possible different interfering scenarios characterized by the same number of jammers via an unsupervised learning clustering fed by a suitable feature set. At the analysis stage, the performance of the devised architecture is investigated over simulated data to highlight the benefits of the approach.

This paper deals with the joint design of transmit waveform and receive filter improving radar Signal to Interference plus Noise Ratio (SINR) in signal-dependent interference and spectrally contested-congested environments. The injected interference energy on each common band is precisely controlled for spectral compatibility. Besides, the transmit signal complies with constant envelop and similarity constraints to bestow some attractive waveform characteristics. To handle the resulting transceiver design problem, an iterative optimization procedure with a polynomial computational complexity is developed leveraging the Coordinate Descent (CD) framework. Finally, numerical results are provided to highlight the effectiveness of the proposed joint transmitter and receiver design technique.

The paper is aimed at discussing the role of cognition in radar sensing providing a system level point of view. In this respect some fundamental issues are considered involving transmitter, receiver, signal and data processing units. Firstly, the definition of the cognitive radar concept is provided and its key aspects are pinpointed. Secondly, the role played by the perception-action cycle and the awareness gleaned by dynamic databases and/or sensing networks is emphasized. Hence the application of the cognitive paradigm for radar operation in spectrally crowded environments is discussed and an example with the use of real hardware is shown. Finally a glimpse on the role of cognition in agile tracking systems is given.

International audience; OLAP is an authoritative analytical tool in the emerging big data analytics context, with particular regards to the target distributed environments (e‧g., Clouds). Here, privacypreserving OLAP-based big data analytics is a critical topic, with several amenities in the context of innovative big data application scenarios like smart cities, social networks, bio-informatics, and so forth. The goal is that of providing privacy preservation during OLAP analysis tasks, with particular emphasis on the privacy of OLAP aggregates. Following this line of research, in this paper we provide a deep contribution on experimenting and assessing a state-of-the-art distributed privacy-preserving OLAP framework, named as SPPOLAP, whose main benefit is that of introducing a completely-novel privacy notion for OLAP data cubes.

Adaptive target detection in the presence of Gaussian interference considering a frequency diverse array multipleinput multiple-output (FDA-MIMO) radar is investigated. At the design stage, adaptive detectors are devised according to the generalized likelihood ratio test (GLRT) criterion, where the target range is assumed unknown within the radar cell. Hence, the maximum likelihood (ML) estimate of the target range under the H 1 hypothesis is approximated either assuming a discrete grid or resorting to a Newton-based optimization procedure. Numerical results are provided to illustrate the effectiveness of the devised detectors.

This paper studies adaptive radar detection of pointlike targets in the presence of Gaussian interference and possible diffuse multipath. For this problem, a Tunable Adaptive Matched Filter (T-AMF) detector which contains a tuning parameter ruled by the strength of multipath effects was proposed. This paper develops a two-stage strategy for the selection of the tuning parameter by jointly using a two-step energy detector and the expected likelihood (EL)-based principle. Once plugged into the T-AMF, the selected parameter yields a fully adaptive detector free of any user parameter. Remarkably, the new architecture possesses the desired constant false alarm rate (CFAR) property with respect to the disturbance covariance. Finally, its detection performance is assessed and validated via numerical examples.

This paper outlines a conceptual and functional digression on a novel Spaceborne Radar (SBR) payload architecture for augmenting space-based measurements and monitoring capabilities for near-Earth Space Situational Awarness (SSA). Taking into account state-of-the art space-qualified technologies in both digital and Radio Frequency (RF) domains, feasible onboard processing subsytems are identified as high level conceptual block schemes allowing for Bayesian inference on small-size debris dynamic states and ancillary scattering signatures. A description of a bespoke Active Electronically Scanned Array (AESA) based Pulse Doppler Radar in the Ka Band is illustrated from a functional perspective built upon a digital modular archetype, along with programmable temporal hierarchies for acquiring a complex data hyper-cube. Operative modes are also addressed including the novel Low Pulse Repetion Frequency (Low-PRF) Range & Range-Rate Search (RRRS) in cascade with a Pause While Scan (PWS) strategy for Bayesian Multiple Target Tracking (MTT).