This paper discusses the problem of streaming coupled tensor factorization based on sensor networks, where each sensor observes only some features of the targets, and the measurements from sensors are provided in a streaming tensor fashion. Moreover, the observed features of different sensors might overlap (i.e., coupled tensor), and there is no central processing unit to collect all sensor data. Then, in our work, the canonical polyadic (CP) decomposition is exploited to perform local tensor decomposition based on the measurements of each sensor, and average consensus (AC) for diffusing information throughout the network. The proposed method is verified via simulations.

In this work a new class of filters, called Lambda:Omicron Multiplicative Error Model (L:OMEM), is introduced with the aim to efficiently address tracking of maneuvering extended objects. In this context, two main challenges have to be tackled: (1) the tracked object moves with unknown time-varying speed and turning rate; (2) the tracked object can produce a large amount of measurements. Closed-form formulas and a novel method to reduce extended object tracking to a conventional point object tracking problem are derived, so that the novel filter results into an accurate and computationally cheap algorithm. Numerical simulations are presented to validate the effectiveness of the proposed approach, where the L:OMEM filter is compared to state-of-the-art filters for extended objects.

This article outlines the archetype of a novel SpaceBorne Radar (SBR) in the Ka-band for space situational awareness (SSA) based on a code-division multiplexing (CDM) multiple-input–multiple-output (MIMO) payload transceiver. Considering small-size hypervelocity debris, the functional architecture of the fully polarimetric SBR is described, including key comparisons with previous works based on single-input–multiple-output (SIMO) configurations. SBR operations are clarified via timing hierarchies in surveillance mode, the complex data hypercube structure, and the low pulse repetition frequency (L-PRF) range and range rate search (RRRS) entailing a track while simultaneous search (TWSS) contacts collection strategy. Ancillary details on the SBR functional architecture provide paramount insights to ponder critical MIMO aspects and pave the way for key research and development efforts. Finally, numerical results provide a proof of concept for the signal processor upstream the constant false alarm rate-like (CFAR-like) detection block.

This article deals with the effects of plasma turbulence on the detection performance of spaceborne radars (SBRs) for space situational awareness (SSA). Physical insights on both channel and target phenomenology lead to reasonable statistical models with a focus on the fading occurrence probability (FOP) in case of weak scintillation. Consequently, the performance analysis of conventional radar detectors in additive white Gaussian noise (AWGN) is provided in a monostatic configuration for either Rayleigh or Rice fluctuating targets, and considering Rice plasma scintillation as a function of the scintillation index$s_{4}$. Numerical results identify a paramount framework to characterize the influence of plasma turbulence on SBR detection performance for SSA. Finally, ancillary notes make provision for tailoring the performance analysis also in the case of bistatic radar configurations.

This letter considers the design of a two-stage direction of arrival (DOA) scheme for radar systems. Precisely, at the first stage a sparse recovery approach is used to obtain both DOA and complex amplitude estimates of the incoming signal. Since the DOA is evaluated on a predefined grid of bins sampling the antenna azimuth mainbeam, at the second stage, a closed-form complex-valued parabolic interpolation is performed to refine it. By doing so, the angle accuracy is improved, but at the same time maintaining fixed the overall computational complexity. Numerical results show the enhancement provided by the proposed procedure to the initial sparse recovery method.

This article delves into a novel spaceborne radar payload transceiver for augmenting space-based monitoring capabilities for near-Earth space situational awareness. The sensor complex data hypercube structure and sensor timing hierarchies in surveillance mode are addressed along with a suitable low pulse repetition frequency range and range-rate search with a pause while scan collection strategy. A bespoke active electronically scanned array-based Pulse Doppler Radar sensor in the Ka-band is then defined in order to cope with the significant Doppler stress characterizing a burst of echoes from hypervelocity debris targets. Before feeding the onboard Bayesian tracker, the complex data hypercube is processed by means of a Doppler filter bank including pulse compression in cascade with a constant false alarm rate-like block.

The sum-product algorithm (SPA) was recently shown to provide a scalable methodology for multitarget tracking (MTT) using multiple sensors. Here, we focus on another advantage of the SPA frame-work, namely, its capacity for Bayesian fusion of heterogeneous data sources and auxiliary information. We develop extensions of the SPA-based multisensor MTT algorithm that integrate data from an auxiliary surveillance system and geographic information about standard target routes. The effectiveness of our approach is demonstrated for a simulated scenario and for a real maritime scenario.

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.

In this letter, the problem of environment classification in the radar context is addressed. Specifically, adaptive architectures are conceived to classify training data, used for covariance estimation, as either homogeneous or heterogeneous. Such architectures are based upon the generalized likelihood ratio test criterion and exploit three covariance matrix structures (i.e., Hermitian, persymmetric, and symmetric structures). Numerical examples based on both synthetic and real data confirm the effectiveness of the proposed algorithms. It is important to highlight that the proposed architectures might represent a preliminary stage whose decisions can be used to select a suitable covariance estimate for target detection purposes.

This paper considers the general problem of tracking a noncooperative target in the presence of multipath. The multipath effect occurs intermittently, according to a discrete-time Markov chain, and exerts an additional unknown measurement error (i.e, a bias), with biases autocorrelated if they occur across successive sampling times. We calculate the posterior Cramér-Rao bound (PCRB) for this problem by augmenting the target state with the multipath bias. An established, efficient Riccati recursion is then used to determine the PCRB, thereby providing mean squared error performance bounds for both the estimation of the target state and the multipath bias. The approach is demonstrated in a simulated scenario in which an airborne radar tracks a low altitude airborne target that is moving in a horizontal plane with nearly constant velocity, using measurements of azimuth. elevation and range. The measurements are intermittently corrupted by multipath bias resulting from specular reflection at the surface boundary. It is shown that the PCRB increases rapidly when the multipath effect occurs, indicating that optimal target tracking performance is significantly degraded at such times. Future work will compare the PCRB developed herein with alternative PCRB methodologies that do not implicitly condition on the multipath effects, and also compare the bound to the performance of a tracking algorithm that is designed to identify and adjust for the multipath effects.

Multiple‐input multiple‐output (MIMO) radars have attracted much attention for their superior ability to enhance a system's performance. In this study, the authors’ goal was the study of the spatial multiplexing gain of MIMO radars with widely separated antennas (WS‐MIMO), which the authors showed that is equal to the number of unambiguously detectable targets. They obtained this number from two different aspects: first, by defining the ambiguity function of a WS‐MIMO radar in the case of multiple targets, suitable for such purpose; Second, by modelling the MIMO radar system with a MIMO wireless channel. They showed that a MIMO radar is indeed a MIMO wireless system communicating the information about the existence of the targets. By such modelling, they could easily make the relation between dual concepts of MIMO radar and MIMO communication, one of which is the multiplexing gain.

We consider the problem of mitigating the effect of malicious cyber-threats spreading across multiple subnets of a data network. Three fundamental issues arise: 1) providing a manageable model of threat propagation; 2) quantifying the danger associated to different subnets; and 3) optimizing the allocation of the countermeasures. We address these issues by providing the following novel contributions. First, a convenient mathematical abstraction of threat propagation is proposed, which employs the birth-and-death process with immigration pioneered by Kendall in his seminal work of 1948. Then, exploiting the notable properties of such a model, we show how to retrieve analytical solutions for optimal resource allocation across subnets, for the case where the parameters of the attack are perfectly known. Finally, the assumption of perfect knowledge is removed, and the unknown attack parameters are estimated using maximum-likelihood estimators.

Phase autofocus is a key step in translational motion compensation for inverse synthetic aperture radar. From the eigenvalue decomposition (EVD) of the covariance matrix generated by the aligned range-compressed signal, eigenvectors can be obtained for effective phase autofocus. However, as the number of pulse samples is increased to improve the cross-range resolution, the high computational complexity of the EVD may become burdensome. To address this problem, we propose a novel EVD-based method in this letter. When the number of range units is larger than the number of pulse samples, the conventional method is used. Otherwise, the transpose of the envelope-aligned data matrix is used to generate a lower dimensional covariance matrix and to perform successive autofocus processing. Since many real targets exist in limited range units, a one- or two-order-higher computational efficiency can be obtained in some typical scenarios with the proposed method, compared with existing EVD-based approaches. Furthermore, the equivalence between the above two methods has been proven in this letter. Finally, the results for real measured data are provided to demonstrate the effectiveness of the proposed method.

In this letter, we address adaptive radar detection of point-like targets in Gaussian clutter with an unknown covariance matrix. To this end, we first exploit the symmetrically structured power spectral density of the clutter to transfer data from the complex to the real domain. Then, the spillover of target energy is incorporated into the design criteria to come up with two architectures capable of guaranteeing improved detection performances and range estimation. The performance assessments, conducted on both simulated data and real recorded datasets, demonstrate the effectiveness of the newly proposed detectors compared with the state-of-the-art counterparts, which ignore either the clutter spectral symmetry or the energy spillover.

Controlling the behavior of a signal defined over a graph by acting on a limited set of nodes is a problem that finds application in many fields. In this paper, we merge recently developed tools in graph signal processing with control theory of complex networks and consider the reconstruction of bandlimited graph signals from their samples through a diffusion process properly driven by a subset of control nodes. Then, we propose an optimization algorithm aimed at minimizing the control energy incorporating a regularization term whose goal is to promote sparsity across time and nodes jointly.

This paper addresses the problem of mitigating non-stationary diffusely scattered multipath interference or “hot clutter” by space-time adaptive processing (STAP) in radar systems that use a multi-channel receive antenna array. A computationally efficient time-varying (TV) fast-time STAP algorithm that can effectively cancel hot clutter during the coherent processing interval (CPI) while simultaneously preserving the Doppler spectrum characteristics of the ordinary backscattered “cold clutter” is proposed. The TV-STAP method is compared with several benchmark techniques in a numerical study simulating aircraft and ship detection in over-the-horizon (OTH) radar.

Current and future radio telescopes, in particular the Square Kilometre Array (SKA), are envisaged to produce large images (> 108pixels) with over 60 dB dynamic range. This poses a number of image reconstruction and technological challenges, which will require novel approaches to image reconstruction and design of data processing systems. In this paper, we sketch the limitations of current algorithms by extrapolating their computational requirements to future radio telescopes as well as by discussing their imaging limitations. We discuss a number of potential research directions to cope with these challenges.

The problem is to establish the presence and subsequently to track a target using multi-static Doppler shift measurements. The assumption is that in the surveillance volume of interest a single transmitter of known frequency is active with multiple spatially distributed receivers collecting and reporting Doppler-shift frequencies. The measurements are affected by additive noise and also contaminated by false detections. The paper develops a Bernoulli particle filter for this application and analyzes its performance by simulations.

This work deals with the analysis of a multisensor radar system in the context of maritime border control scenario. The system is composed by a receiver that exploits the signal emitted by two non co-operative transmitters of opportunity: a UMTS base station and a FM radio station. An algorithm for selecting the transmitter for the tracking of a radar target is proposed. This algorithm can provide a significant aid to harbour protection and can reduce the computational load of surveillance operations.

The problem of blind source separation by spatial processing is considered for convolutive mixtures of narrowband sources received by a sensor array. Multipath propagation is due to time-varying doubly-spread finite impulse response channels where the signal modes are Doppler shifted and arise from diffuse scattering rather than specular reflection. This paper introduces a new blind spatial filtering technique, referred to as the Generalized Estimation of Multipath Signals (GEMS) algorithm, which exploits multipath as the basis for signal separation under relatively mild assumptions regarding the signal spatial signatures and source waveform properties. The performance of GEMS is compared with a benchmark signal-copy procedure using real data acquired by an experimental high frequency (HF) antenna array system.1

This work deals with the Sequential Cramér-Rao Lower Bound (SCRLB) for sequential target state estimators for a bistatic tracking problem. In the context of tracking, the SCRLB provides a powerful tool, enabling one to determine a lower bound on the optimal achievable accuracy of target state estimation. The bistatic SCRLBs are analyzed and compared to the monostatic counterparts for a fixed target trajectory. Two different kinematic models are analyzed: constant velocity and constant acceleration. The derived bounds are also valid when the target trajectory is characterized by the combination of these two motions.

In this paper, we consider the problem of detecting an unknown signal, belonging to a conic region, in the presence of complex Gaussian disturbance. We assume that the cone aperture parameter, which ranges in a pre-assigned interval, is unknown. Hence, we devise two decision rules which are respectively based on the Generalized Likelihood Ratio Test (GLRT) and on the Multifamily Likelihood Ratio Test (MFLRT). Both the receivers ensure the Constant False Alarm Rate (CFAR). At the analysis stage, we compare the performance of the two decision strategies in correspondence of different parameters for the useful signal component.

In this paper we compare two radar target direction-of-arrival (DOA) estimation algorithms, the classical moving window (MW) and the asymptotic maximum likelihood (AML) estimators. The first technique for azimuth DOA estimation exploits multiple detections in the same time-on-target and the second one exploits the fact that the radar antenna mechanical scanning impresses an amplitude modulation on the signals backscattered by the target. Performances of the estimators are numerically investigated through Monte Carlo simulation in terms of root-mean-square-error (RMSE), probability of detection for a fixed probability of false alarm, and probability of "splitting". The obtained results show that the asymptotic maximum likelihood estimator generally outperforms the classical moving window estimator.

The paper develops an information fusion system that aims at supporting a commander's decision making by providing an assessment of threat, that is an estimate of the extent to which an enemy platform poses a threat based on evidence about its intent and capability. Threat is modelled in the framework of the valuation-based system (VBS), by a network of entities and relationships between them. The uncertainties in the relationships are represented by belief functions as defined in the theory of evidence. Hence the resulting network for reasoning is referred to as an evidential network. Local computations in the evidential network are carried out by inward propagation on the underlying joint binary tree. This allows the dynamic nature of the external evidence, which drives the evidential network, to be taken into account by recomputing only the affected paths in the joint binary tree.

High frequency skywave and surface-wave over-the-horizon (OTH) radars are required to detect targets in the presence of powerful clutter and interference from man-made and natural sources. However, the received disturbances may be highly structured in the time domain (i.e. pulse-to-pulse) within the coherent processing interval (CPI). This provides scope for adaptive Doppler processing to enhance detection performance with respect to conventional FFT-based methods. This paper proposes a generalized likelihood ratio test (GLRT) based detector that not only possesses the valuable constant false alarm rate (CFAR) property invariant to disturbance scale-change, but also exhibits distinct advantages over the adaptive coherence estimator (ACE) and adaptive subspace detector (ASD) when unwanted signals are present. Here, we present the first experimental results for this detector in a HF surface wave radar Doppler processing application.

In this paper we investigate the k-connectivity threshold of distributed dense ad hoc heterogeneous wireless sensor network architecture. We consider the situation when sensors are deployed in the surveillance area according to a uniform distribution perturbed by a Gaussian noise. We derive analytically the minimum detection range which guarantees an emerging structure in the network, namely the connectivity, which becomes larger and larger as the number of sensors in the network increase. This allows the target track to be propagated almost surely throughout the network using the minimum possible amount of prime energy. We report the results of some simulation experiments which further support the theoretical results.

This paper compares two different approaches for sensor selection for distributed tracking: 1) The Frisbee method, and 2) Global Node Selection (GNS). The Frisbee method is based on the proximity of the nodes to the predicted location of the target; GNS is based on minimizing the unbiased Cramer Rao lower bound (CRLB). Both theoretical and experimental results indicate that the Frisbee method is as effective as GNS. Furthermore, the Frisbee method is attractive due to its very light computational load.

This work presents a deterministic approach to the problem of modelling the human behaviour in a command and control radar system and it considers the fusion of information between the operator and the system. The implementation and the results of a case study are presented where a human operator performs a tracking operation of multiple targets in a sea region. The mission performed by the operator is the surveillance of a coast area and the selection of a system action against possible threat targets, in order to check their identity. An analytical model of human memory has been investigated where the human decision maker is represented as a subsystem involved with two operational blocks, corresponding to the situation assessment process and the response selection process that he performs. The operator performance is evaluated by mean of his error probability in these two processes

This paper illustrates an emulator of an integrated system developed for predicting its performance in a typical border control surveillance scenario. The sensor suite emulated is constituted by: one spaceborne surveillance radar, one airborne radar for long range surveillance, one radar devoted to vessel traffic control (VTC) and one infrared (IR) camera. The collected data are sent to the command & control centre which allocates the proper means for facing the "potentially hostile" target intrusion. Reaction time and probability of correct reaction in presence of an unidentified moving object have been selected as representative of the integrated system performance. A key point to note is that the visualization is nowadays considered as an enabling technology; thus a great care has been dedicated to the presentation of results via movies realization

The paper deals with the problem of impact point prediction of ballistic targets (BT) by processing measurements acquired by two 3D surveillance radars. It is assumed that the radars acquire a limited number of measurements that do not encompass the whole target trajectory; thus the established target track has to be extrapolated ahead in time in order to predict the coordinates of the impact point. The updating and testing of the data extractor (DE) of a notional tri-dimensional surveillance radar system is presented in this paper; the modified hardware and software is capable of acquiring, managing and fusing tracks pertaining to the radar system housing the DE and to other systems connected to the DE itself

The paper presents an analysis of the phased array radar allocation demands, when tracking highly maneuverable anti-ship missiles (ASM) using a collocated radar/IRST sensor combination. The motion of the ASM is modeled using the quantized acceleration levels. The principal aim of this analysis is to determine an upper bound on the average radar update time. This bound follows from a Cramer-Rao type error bound for the estimation of linear jump Markov dynamic systems. Given a dynamic motion model of an ASM, the IRST/radar sensor characteristics and a tolerable level of target state estimation error, we can theoretically predict the maximum average update time required for the phased-array radar. The presented analysis allows us to quantify the IRST benefits in ASM defence, without a need for extensive Monte Carlo simulations

A new centroid formula for range-free sensor node location estimation using the proximity anchor positions is proposed. A detailed statistical analysis of the estimator is presented in one-dimension, taking into account the boundary conditions. The estimator is optimal in the minimum-mean square error sense for the one-dimensional case and outperforms the averaging centroid formula for N>2, where N is the number of proximity anchors

This work deals with the problem of detecting and estimating multiple radar targets present in the same range-azimuth resolution cell of a surveillance radar system with a mechanically rotating antenna. First, the target parameters are estimated assuming a maximum number of possible targets. To this purpose we use the asymptotic maximum likelihood (AML) RELAX estimator derived in the first part (for pt. 1 see ibid., p. 529-532 (2003)). Subsequently, these estimates are used in a sequential hypotheses test (SHT) procedure. The statistic of the test at each step of the SHT procedure is derived using an asymptotic expression of the generalized likelihood ratio test (GLRT) statistic. An upper bound of the false alarm probability is derived in closed form, whereas detection performance of the proposed SHT detector is investigated through Monte Carlo simulation.

Phase unwrapping is the key problem in building the elevation map of a scene from interferometric synthetic aperture radar (SAR) system data. Phase unwrapping consists in the reconstruction of the phase difference of the radiation received by two SAR systems as a function of the azimuth and slant range coordinates. The data available to reconstruct the phase difference are a measure of the difference module 2/spl pi/. The authors propose a phase unwrapping method that makes use of the equivalent, in a discrete space, of the irrotational property of a gradient vector field. This property is used first to locate the areas where the discrete vector field estimated from the available data must be corrected, and then, with the knowledge of some a priori information, to perform the correction needed to obtain a useful estimate of the discrete gradient of the phase difference function, from which the phase difference function is reconstructed. The use of the fast Fourier transform makes it possible to have a fast algorithm, that is to process an image of N pixel in O(NlogN) elementary operations. Tests of the method proposed here on real and simulated data are presented.

Impulsive interference might increase the number of false alarms in a surveillance radar. The sidelobe blanking (SLB) device, used in connection with the radar system, reduces the number of false alarms. This paper presents closed-form expressions of the probability (P/sub B/) of blanking an impulsive interference via an SLB device for two statistical models of pulsed interference, namely: (i) known amplitude and random phase and (ii) random amplitude and random phase. A closed-form expression of P/sub B/ is found for the interference model ii. Performance curves are presented and compared for the two interference models.