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Location based services are expected to play a major role in future generation cellular networks, starting from the incoming 5G systems. At the same time, localization technologies may be severely affected by attackers capable to deploy low cost fake base stations and use them to alter localization signals. In this paper, we concretely focus on two classes of threats: noise-like jammers, whose objective is to reduce the signal-to- noise ratio, and spoofing/meaconing attacks, whose objective is to inject false or erroneous information into the receiver. Then, we formulate the detection problems as binary hypothesis tests and solve them resorting to the generalized likelihood ratio test design procedure as well as the Latent Variable Models, which involves the expectation-maximization algorithm to estimate the unknown data distribution parameters. The proposed techniques can be applied to a large class of location data regardless the subsumed network architecture. The performance analysis is conducted over simulated data generated by using measurement models from the literature and highlights the effectiveness of the proposed approaches in detecting the aforementioned classes of attacks.

In this paper, we propose a new solution for the detection problem of a coherent target in heterogeneous environments. Specifically, we first assume that clutter returns from different range bins share the same covariance structure but different power levels. This model meets the experimental evidence related to non-Gaussian and non-homogeneous scenarios. Then, unlike existing solutions that are based upon estimate and plug methods, we propose an approximation of the generalized likelihood ratio test where the maximizers of the likelihoods are obtained through an alternating estimation procedure. Remarkably, we also prove that such estimation procedure leads to an architecture possessing the constant false alarm rate (CFAR) when a specific initialization is used. The performance analysis, carried out on simulated as well as measured data and in comparison with suitable well-known competitors, highlights that the proposed architecture can overcome the CFAR competitors and exhibits a limited loss with respect to the other non-CFAR detectors.

This paper addresses the challenge of classifying polarimetric SAR images by leveraging the peculiar characteristics of the polarimetric covariance matrix (PCM). To this end, a general framework to solve a multiple hypothesis test is introduced with the aim to detect and classify contextual spatial variations in polarimetric SAR images. Specifically, under the null hypothesis, only an unknown structure is assumed for data belonging to a 2-dimensional spatial sliding window, whereas under each alternative hypothesis, data are partitioned into subsets sharing different structures. The problem of partition estimation is solved by resorting to hidden random variables representative of covariance structure classes and the expectation-maximization algorithm. The effectiveness of the proposed detection strategies is demonstrated on both simulated and real polarimetric SAR data also in comparison with existing classification algorithms., 13 pages, 11 figures

We design the proof of concept for high-resolution (HR) real-time (RT), Geosynchronous and Geostationary (Geo) Polarimetric (Pol) using orbital angular momentum (OAM) interferometry - radio detection and ranging (RADAR) (HR-RT-GeoPolInt-OAM-RADAR) and multi-chromatic analysis (MCA) extended to Tomography (HR-RT-GeoPolInt-OAM-MCA-TomoRADAR). The OAM interferometry communication channel is generated by two fixed sources distanced by a given spatial baseline and used for range-azimuth synthesis. The frequency channel, instead, is used to provide information about the altitude. Finally, the information encoded in the polarization of the electromagnetic (EM) waves, which is related to the Spin Angular Momentum (SAM), is used to synthesize full-Pol RADAR images, with technological redundancy. Here we present the design of a planar vortex antenna, tailored for Geo applications, where the imaging system transmits ''ad-hoc`` structured wave packets using an incremental stepped chirp strategy and with single-mode OAM linearly incremented modulation. We assign the resolutions of each dimension to three bands assumed by the EM wave. The radial and tangential components received from the HR-RT-GeoPolInt-OAM communication channel backscattered signals are used to focus, through fast-Fourier transform (FFT) techniques, a range-azimuth image that belongs to a single epoch at a given constant frequency. Each OAM fast-time RADAR image is separated in frequency by using MCA. This procedure is repeated for all the epochs of the entire stepped-frequency chirp. Once each two-dimensional image is synthesized, they are co-registered, and the HR-RT-GeoPolInt-OAM-MCA-TomoRADAR slices are focused in altitude by using FFT techniques. Range-azimuth and tomographic resolutions depend on the OAM value and the stepped frequency chirp bandwidths., 18 pages, 15 figures

This paper reports the results of a study on the estimation of the deformation of the Italian Campotosto Lake dams and bridge due to the earthquake series recently occurring in the Central of Italy and still ongoing. The analysis is performed by processing a stack of 180 synthetic aperture radar (SAR) interferometric images, observed by the Italian satellite system COSMO-SkyMed (CSK) and 74 interferometric SAR images, collected by the European Space Agency (ESA) satellite system Sentinel. Results show an ongoing negative subsidence at the location of the main Campotosto lake dam infrastructure and the main bridge connecting two opposite roads. The work reveals information about the Earth surface deformation, which might be useful in order to track the subsidence caused by the recent earthquake series occurring in the center of Italy and, hence, for a correct administration of the dam and an efficient technical maintenance of the local territory.

Damage in civil engineering structures can be represented by a reduction of the structural bearing capacity during their service period. This reduction is usually caused by degradation of materials, structural components or connections due to environmental phenomena leading to excessive loading effects. Typical damages in civil engineering structures include cracks, fatigue, steel corrosion, concrete spalls, scour and deterioration. Undetected damage can lead to structural failure causing loss of human life. Considering these problems it is necessary to detect early damage within a structure, in order to undertake appropriate repairs as early as possible. The main issue of early warning infrastructure fault detection is that expensive in-situ distributed monitoring sensor networks has to be installed. This research propose a new global infrastructure monitoring paradigm using micro-motion (m-m) estimation of critical sites, from space borne Synthetic Aperture Radar (SAR) data, in this case. In order to apply this method for damage detection, an approach using modal proprieties is applied. m-m is processed to extract modal features such as natural frequencies and mode shapes. The case study of the Morandi bridge (Polcevera Viaduct) in Genoa (Italy) is considered in this paper and the proposed method shows abnormal vibrational modes during the period before the collapse of the bridge.

In this paper, we develop a new elegant framework relying on the Kullback-Leibler Information Criterion to address the design of one-stage adaptive detection architectures for multiple hypothesis testing problems. Specifically, at the design stage, we assume that several alternative hypotheses may be in force and that only one null hypothesis exists. Then, starting from the case where all the parameters are known and proceeding until the case where the adaptivity with respect to the entire parameter set is required, we come up with decision schemes for multiple alternative hypotheses consisting of the sum between the compressed log-likelihood ratio based upon the available data and a penalty term accounting for the number of unknown parameters. The latter rises from suitable approximations of the Kullback-Leibler Divergence between the true and a candidate probability density function. Interestingly, under specific constraints, the proposed decision schemes can share the constant false alarm rate property by virtue of the Invariance Principle. Finally, we show the effectiveness of the proposed framework through the application to examples of practical value in the context of radar detection also in comparison with two-stage competitors. This analysis highlights that the architectures devised within the proposed framework represent an effective means to deal with detection problems where the uncertainty on some parameters leads to multiple alternative hypotheses.

The received signal in Rician multipath channels is modeled as a complex constant, accounting for the main (LoS) path, added to a circular complex Gaussian random variable, modeling all the diffusive secondary paths. The Rician K-factor is accordingly defined as the ratio between the powers of LoS and diffusive components. Under this (widely assumed) multipath model, a closed-form expression for the Rician K-factor estimate is commonly obtained exploiting the second and fourth moments of the received signal amplitude. However, in mobile communications, random shadowing effects make the LoS component fluctuating (Rician shadowed model) leading to a performance degradation of the conventional estimator. With focus on land mobile satellite communications, in this letter, a novel estimation procedure of the Rician K-factor, aimed at avoiding the bias error of the conventional estimator when the LoS component is Nakagami-m distributed, is proposed and assessed. Specifically, the estimate is computed using the solution of a system of three equations and three unknowns in conjunction with the likelihood function of the received signal. The performance analysis shows that the new estimator can guarantee better performance than the conventional estimator.

The lack of availability of historical and reliable river water level information is an issue that can be overcome through the exploitation of modern satellite remote sensing systems. This research has the objective of contributing in solving the information-gap problem of river flow monitoring through a synthetic aperture radar (SAR) signal processing technique that has the capability to perform water flow elevation estimation. This paper proposes the application of a new method for the design of a robust procedure to track over the time double-bounce reflections from bridges crossing rivers to measure the gap space existing between the river surface and bridges. Specifically, the difference in position between the single and double bounce is suitably measured over the time. Simulated and satellite temporal series of SAR data from COSMO-SkyMed data are compared to the ground measurements recorded for three gauges sites over the Po and Tiber Rivers, Italy. The obtained performance indices confirm the effectiveness of the method in the estimation of water level also in narrow or ungauged rivers.

Se realizó un estudio para evaluar la producción y calidad de forraje de tres cultivares de pasto Pennisetum purpureum Shum para aprovechar su potencial forrajero, en el Centro Nacional de Investigaciones Agropecuaria en Managua, situado a una altura de 56 msnm, temperatura media desde 26.9 ºC. La precipitación media es de 1119.8 mm, no se utilizó fertilizantes químicos u orgánicos , ni riego. Se evaluó la altura, relación hoja/ tallo, número de macollas, producción de forraje verde, materia seca, proteína, fibra detergente ácida y fibra detergente neutra del pasto sometido a tres frecuencias de corte (55, 65 y 75 días). Se utilizó un diseño experimental de bloque completo al azar (BCA) con arreglo factorial, con cuatro bloques, tres tratamientos (Maralfalfa, King grass y CT-115), tres frecuencias de corte (55, 65 y 75 días) y cuatro repeticiones por tratamiento. Cada bloque estaba conformado por 9 parcelas. El análisis estadístico de los resultados (P

This paper deals with detection of oil spills from multi-polarization 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 oil spills 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 oil spills leads to a signal, in some eigen-directions, weaker than the one gathered from a reference area where it is a-priori known the absence of any oil slicks. A Multi-family Generalized Likelihood Ratio Test (MGLRT) approach is pursued to come up with an adaptive detector ensuring the Constant Alarm False Rate (CFAR) property. At the analysis stage, the behavior of the new architecture is investigated in comparison with a benchmark (but non-implementable) structure and some other sub-optimum adaptive 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.

In this paper we investigate how enforcement of regulation affects the size of irregular sector, firm perfomance and the exit rate to the market. Three kinds of enforcement policy will be tested in the model: control, punish and legitimacy. The first policy is based on the number of inspectors present in the economy; the second is defined by the magnitude of punish; the third is measured by the social legitimacy. Our results show the negligible influence of control to enforce irregularity; the strong effect of punish on irregular sector with a high exit rate; the good effect of legitimacy policy in promoting regularity with a low output performance.

Este trabajo se realizó en la granja porcina del Instituto Superior de Ciencias Agropecuarias (ISCA), ubicada en la Hacienda Santa Rosa km, 12 ½ carretera norte, Managua, Las variables climáticas en promedio se comportaron de la siguiente manera: precipitación 2.9mm, temperatura medias mensuales 26.9ºc y húmedad relativa 65.8%. en 6% se utilizaron cinco cerdas landrace – Yorkshire distribuidas aleatoriamente en un diseño completamente al azar, con cinco tratamientos y 9 repeticiones cada uno, tomando como repetición cada cerdito de la camada: se suministró concentrado a las crías a los 7, 14.21, 33 y 42 días de edad respectivamente; para estudiar el comportamiento de las crías hasta los 87 días de edad; asì mismo evaluara el comportamiento (pérdidas de peso) de las madres y su estado físico al final de la lactación. Los resultados mostraron que no existe diferencia estadísticas entre los distintos tratamientos para el peso promedio en (Kg) de las crías a los 42 días (destete); pero sí se observó un mayor peso (P

In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we present a novel approach based on a three-stage processing chain that involves the Sparse Learning via Iterative Minimization algorithm, the k-means clustering method and the ad hoc detector by exploiting the sparse nature of the operating scenario. Under the latter assumption, the detection strategy declares the presence of targets subsequently to the retrieval of their corresponding tracks performed by jointly processing the received echoes of multiple consecutive radar scans. Simulation results show that the proposed approach is able to provide good tracking and detection capabilities for different multiple target trajectories with low Signal-to-Interference-plus-Noise ratio and results in providing advantages when compared to a number of other reference Track-Before-Detect strategies based on sparse data processing techniques.

In this paper, we address the problem of classifying clutter returns in order to partition them into statistically homogeneous subsets. The classification procedure relies on a model for the observables including latent variables that is solved by the expectation-maximization algorithm. The derivations are carried out by accounting for three different cases for the structure of the clutter covariance matrix. A preliminary performance analysis highlights that the proposed technique is a viable means to cluster clutter returns over the range., 12 pages, 13 figures

In this paper, we address the problem of detecting a point-like target embedded in clutter characterized by a symmetrically structured power spectral density and persymmetric covariance matrix. In particular, we consider the so-called partially homogeneous environment, where the cell under test and the training samples share the same covariance matrix up to an unknown power scaling factor. At the design stage, we jointly exploit the spectral properties of the clutter and the persymmetric structure of the clutter covariance matrix to reformulate the decision problem in terms of real variables with an increased number of training samples. Then, we derive two adaptive detectors relying on the Rao test and a suitable modification of the generalized likelihood ratio test (GLRT). The performance analysis, conducted on both simulated and real radar data, confirms the superiority of the newly proposed receivers over the traditional state-of-the-art counterparts which ignore either the persymmetry or the symmetric spectrum.

This paper addresses adaptive radar detection of dim moving targets. To circumvent range migration, the detection problem is formulated as a multiple hypothesis test and solved applying model order selection rules which allow to estimate the "position" of the target within the CPI and eventually detect it. The performance analysis proves the effectiveness of the proposed approach also in comparison to existing alternatives., Comment: 5 pages, 2 figures, submitted to IEEE Signal Processing Letters

In this letter, an adaptive approach to classify the structure of the Interference Covariance Matrix (ICM) is proposed. It extends the framework of [1] to the heterogeneous environment where the secondary radar data used to estimate the ICM share the same covariance structure but different power levels. In particular, the considered classification problem is formulated in terms of a multiple hypothesis test and the Principle of Invariance is exploited to replace original data with a suitable statistic whose distribution is independent of the power scaling factors. Then, classification schemes are devised resorting to model order selection rules. At the analysis stage, the effectiveness of the newly devised classifiers is illustrated over simulated data as well as radar measured data.

Poster Abstract non previsto

This paper deals with the problem of adaptive vector subspace signal detection in partially homogeneous Gaussian disturbance and structured (unknown) deterministic interference within the framework of invariance theory. It is first proved that the Maximal Invariant Statistic (MIS) for the problem at hand is scalar-valued and coincides with the well-known adaptive normalized matched filter evaluated after data projection in the complementary subspace of the interfering signal. Second, the statistical characterization of the MIS under both hypotheses is derived. Then, it is shown the statistical equivalence of (two-step) generalized-likelihood ratio test, Rao and Wald tests, as well as the more recently considered Durbin and Gradient test, to the above statistic. Finally, simulation results are provided to confirm our findings and analyze the performance trend of the MIS with the relevant parameters.

We address adaptive radar detection of targets embedded in clutter characterized by a symmetrically structublack power spectral density (PSD) and persymmetric covariance matrix. At the design stage, such properties are jointly exploited to come up with decision schemes capable of guaranteeing superior detection performances with respect to architectures which incorporate either persymmetry or clutter PSD symmetry. The performance analysis, both on simulated and on real radar data, confirms the superiority of the newly proposed architectures over their natural counterparts which do not take advantage of both the sources of a priori information.

In the present paper, we focus on the design of a Kalman-based algorithm to track a single target in range. The proposed approach exploits spillover of target energy between consecutive matched filter samples; to this end, at the kth scan, a GLRT-based detector is fed by the predicted target position (computed by the tracker using measurements up to the (k-1)th scan) and returns to the tracker an estimate of the current target position (measurement) obtained by taking into account target strength over consecutive samples. We assume that the target echo is known up to a complex deterministic parameter; moreover, we suppose that the target is embedded in white Gaussian noise with unknown power. The performance analysis is conducted in comparison to a more conventional Kalman-based tracker that interpolates consecutive samples to take advantage of the spillover. Remarkably, the proposed estimation scheme can provide accurate estimates of the target position within the cell under test.

In this study, the authors propose a new tracking algorithm for multistatic sonar systems, where the measurements collected by different sensors are sent to a fusion centre. The proposed algorithm relies on the main idea behind the track-before-detect paradigm, which consists of processing data from several consecutive pings, and estimates target positions by maximising the likelihood function of the available measurements. The authors assume that one manoeuvring target is present within the surveillance area. The preliminary performance assessment, carried out on simulated scenarios, shows that the proposed algorithm has acceptable performance also when the probability of detection per sensor is low (in the order of 0.3) and measurement errors are significant.

This paper deals with the problem of adaptive multidimensional/multichannel signal detection in homogeneous Gaussian disturbance with unknown covariance matrix and structured (unknown) deterministic interference. The aforementioned problem extends the well-known Generalized Multivariate Analysis of Variance (GMANOVA) tackled in the open literature. In a companion paper, we have obtained the Maximal Invariant Statistic (MIS) for the problem under consideration, as an enabling tool for the design of suitable detectors which possess the Constant False-Alarm Rate (CFAR) property. Herein, we focus on the development of several theoretically-founded detectors for the problem under consideration. First, all the considered detectors are shown to be function of the MIS, thus proving their CFARness property. Secondly, coincidence or statistical equivalence among some of them in such a general signal model is proved. Thirdly, strong connections to well-known simpler scenarios found in adaptive detection literature are established. Finally, simulation results are provided for a comparison of the proposed receivers., Submitted for journal publication

We deal with the problem of detecting point-like targets in diffuse multipath environments, modeling the target echo as the superposition of a deterministic signal with an unknown scaling factor (due to the direct path) plus a zero-mean complex circular symmetric Gaussian random vector with an unknown covariance matrix (accounting for the echoes from the glistening surface). We devise a constrained Generalized Likelihood Ratio Test (GLRT) for the resulting hypothesis testing problem, enforcing the primary data covariance matrix (due to both interference and multipath echoes) to belong to a neighborhood of the secondary data sample covariance matrix. Remarkably, the proposed decision scheme ensures the desirable Constant False Alarm Rate (CFAR) property with respect to the unknown parameters of the interference. The performance assessment, conducted on simulated data in terms of detection probability also in comparison with existing solutions, highlights the effectiveness of the new approach to cope with diffuse multipath phenomena.

In this study, the authors derive an adaptive decision scheme with enhanced range estimation capabilities for point-like targets in partially homogeneous environments. To this end, the authors jointly exploit the oversampling of the received signal and the spillover of target energy to consecutive range samples. A suitable discrete-time model for the received signal is introduced and the so-called two-step generalised likelihood ratio test is derived and assessed. The performance analysis, conducted resorting to both simulated data and real recorded datasets, is aimed at assessing the effectiveness of the proposed solution, also in comparison with the existing detectors with range estimation capabilities. The illustrative examples highlight that better detection performances and increased range estimation accuracy can be achieved exploiting oversampling at the price of an additional processing cost.

In this correspondence we propose track-before-detect (TBD) strategies for space-time adaptive processing (STAP) radars. As a preliminary step we introduce the target and noise models in discrete-time form. Then, resorting to generalized likelihood ratio test (GLRT)-based and ad hoc procedures we derive detectors for two different scenarios (a point better clarified in the body of the correspondence). The preliminary performance assessment, conducted resorting to Monte Carlo simulation, shows that the proposed procedures might be viable means to implement early detection and track initiation of weak moving targets.

In this paper we propose and assess TBD strategies for radar systems adopting STAP. At the design stage we consider a radar system equipped with a linear array of Na sensors that illuminates the surveillance area by transmitting M coherent pulse trains, each consisting of Np pulses, before deciding whether or not a target is present over L adjacent range cells. As a preliminary step we introduce the target and noise models. Then, resorting to GLRT and ad hoc procedures, we derive adaptive CFAR detectors for both stationary and scan-to-scan varying clutter scenarios (a point better clarified in the body of the paper). The proposed detectors guarantee the CFAR property with respect to the unknown spectral properties of the clutter. The preliminary performance assessment, conducted resorting to Monte Carlo simulation, shows that proposed procedures might be a viable means to implement early detection and track initiation of weak moving targets.