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  • 1.
    Boström-Rost, Per
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Axehill, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    PMBM Filter With Partially Grid-Based Birth Model With Applications in Sensor Management2022In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 58, no 1, p. 530-540Article in journal (Refereed)
    Abstract [en]

    This article introduces a Poisson multi-Bernoulli mixture (PMBM) filter in which the intensities of target birth and undetected targets are grid-based. A simplified version of the Rao-Blackwellized point mass filter is used to predict the intensity of undetected targets and to initialize tracks of targets detected for the first time. The grid approximation can efficiently represents intensities with abrupt changes with relatively few grid points compared to the number of Gaussian components needed in conventional PMBM implementations. This is beneficial in scenarios where the sensors field of view is limited. The proposed method is illustrated in a sensor management setting, where trajectories of sensors with limited fields of view are controlled to search for and track the targets in a region of interest.

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  • 2.
    Boström-Rost, Per
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Axehill, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Sensor management for search and track using the Poisson multi-Bernoulli mixture filter2021In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 57, no 5, p. 2771-2783Article in journal (Refereed)
    Abstract [en]

    A sensor management method for joint multi-target search and track problems is proposed, where a single user-defined parameter allows for a trade-off between the two objectives. The multi-target density is propagated using the Poisson multi-Bernoulli mixture filter, which eliminates the need for a separate handling of undiscovered targets and provides the theoretical foundation for a unified search and track method. Monte Carlo simulations of two scenarios are used to evaluate the performance of the proposed method.

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  • 3.
    Da Fontoura, A. A.
    et al.
    Federal University of Rio Grande do Sul, Brazil .
    Nascimento, F.A. M.
    Federal University of Rio Grande do Sul, Brazil .
    Nadjm-Tehrani, Simin
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    De Freitas, E. P.
    Federal University of Rio Grande do Sul, Brazil .
    Timing Assurance of Avionic Reconfiguration Schemes using Formal Analysis2020In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, IEEE Transactions on Aerospace and Electronic Systems, E-ISSN 1557-9603, Vol. 56, no 1, p. 95-106Article in journal (Refereed)
    Abstract [en]

    Reconfigurable avionics systems can tolerate faults by moving functionalities from failed components to another available system component. This paper proposes a distributed reconfigurable architecture for application migration from failed modules to working ones. The feasible system reconfiguration states are determined off-line to provide the expected configuration in foreseen situations. Model Checking is used to determine feasible configurations evaluating specific temporal properties. A case study is used to show the application of the presented approach as a proof of concept

  • 4.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Extended Target Tracking Using a Gaussian-Mixture PHD Filter2012In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 48, no 4, p. 3268-3286Article in journal (Refereed)
    Abstract [en]

    This paper presents a Gaussian-mixture implementation of the phd filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.

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  • 5.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, U.
    Middle E Technical University, Turkey.
    New Prediction for Extended Targets With Random Matrices2014In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 50, no 2, p. 1577-1589Article in journal (Refereed)
    Abstract [en]

    This paper presents a new prediction update for extended targets whose extensions are modeled as random matrices. The prediction is based on several minimizations of the Kullback-Leibler divergence (KL-div) and allows for a kinematic state dependent transformation of the target extension. The results show that the extension prediction is a significant improvement over the previous work carried out on the topic.

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  • 6.
    Larsson, Erik G.
    et al.
    Dept. of Electrical and Computer Engineering, University of Florida, USA.
    Li, Jian
    Dept. of Electrical and Computer Engineering, University of Florida, USA.
    Spectral Analysis of Periodically Gapped Data2003In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 39, no 3, p. 1089-1097Article in journal (Refereed)
    Abstract [en]

    We devise novel, interpolation-free, and computationally tractable extensions of the spectral analysis methods Capon and APES (amplitude and phase estimation) to periodically gapped data. Our methods are based on the observation that periodically gapped data usually have a structure that supports estimation of a relatively large number of covariance lags. The large signal-to-noise-ratio (SNR) behavior of the new algorithms is discussed, and numerical examples are provided to illustrate their performance.

  • 7.
    Larsson, Erik G.
    et al.
    Dept. of Electrical and Computer Engineering, University of Florida, USA.
    Liu, Guoqing
    Dept. of Electrical and Computer Engineering, University of Florida, USA.
    Stoica, Petre
    Department of Systems and Control, Uppsala University, Sweden.
    Li, Jian
    Dept. of Electrical and Computer Engineering, University of Florida, USA.
    High-resolution SAR imaging with angular diversity2001In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 37, no 4, p. 1359-1372Article in journal (Refereed)
    Abstract [en]

    We propose to use the APES (amplitude and phase estimation) approach for the spectral estimation of gapped data and synthetic aperture radar (SAR) imaging with angular diversity. A relaxation-based algorithm, referred to as GAPES (Gapped-data APES), is proposed, which includes estimating the spectrum via APES and filling in the gaps via a least squares (LS) fitting. For SAR imaging with angular diversity data fusion, we perform one-dimensional (1-D) windowed fast Fourier transforms (FFTs) in range, use the GAPES algorithm to interpolate the gaps in the aperture for each range, apply 1-D inverse FFTs (IFFTs) and dewindow in range, and finally apply the two-dimensional (2-D) APES algorithm to the interpolated matrix to obtain the 2-D SAR image. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

  • 8.
    Nordlund, Per-Johan
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    Marginalized Particle Filter for Accurate and Reliable Terrain-Aided Navigation2009In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 45, no 4, p. 1385-1399Article in journal (Refereed)
    Abstract [en]

    This paper details an approach to the integration of INS (Inertial Navigation System) and TAP (Terrain-Aided Positioning). The solution is characterized by a joint design of INS and TAP, meaning that the highly nonlinear TAP is not designed separately but jointly with the INS using one and the same filter. The applied filter extends the theory of the MPF (Marginalized Particle Filter) given by [1]. The key idea with MPF is to estimate the nonlinear part using the particle filter and the part which is linear, conditionally upon the nonlinear part, is estimated using the Kalman filter. The extension lies in the possibility to deal with a third multi-modal part, where the discrete mode variable is also estimated jointly with the linear and nonlinear parts. Conditionally upon the mode and the nonlinear part, the resulting subsystem is linear and estimated using the Kalman filter. Given the nonlinear motion equations which the INS uses to compute navigation data, the INS equations must be linearized for the MPF to work. A set of linearized equations is derived and the linearization errors are shown to be insignificant with respect to the final result. Simulations are performed and the result indicates near-optimal accuracy when compared to the Cramer-Rao lower bound.

  • 9.
    Nordlund, Per-Johan
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Probabilistic Noncooperative Near Mid-Air Collision Avoidance2011In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 47, no 2, p. 1265-1276Article in journal (Refereed)
    Abstract [en]

    We propose a probabilistic method to compute the near mid-air collision risk as a function of predicted flight trajectory. The computations use state estimate and covariance from a target tracking filter based on angle-only sensors such as digital video cameras. The majority of existing work is focused on risk estimation at a certain time instant. Here we derive an expression for the integrated risk over the critical time horizon. This is possible using probability for level-crossing, and the expression applies to a three-dimensional piecewise straight flight trajectory. The Monte Carlo technique provides a method to compute the probability, but a huge number of simulations is needed to get sufficient reliability for the small risks that the applications require. Instead we propose a method which through sound geometric and numerical approximations yield a solution suitable for real-time implementations. The algorithm is applied to realistic angle-only tracking data, and shows promising results when compared to the Monte Carlo solution.

  • 10.
    Pinto, Juliano
    et al.
    Chalmers Univ Technol, Sweden.
    Hess, Georg
    Chalmers Univ Technol, Sweden.
    Ljungbergh, William
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Xia, Yuxuan
    Chalmers Univ Technol, Sweden.
    Wymeersch, Henk
    Chalmers Univ Technol, Sweden.
    Svensson, Lennart
    Chalmers Univ Technol, Sweden.
    Deep Learning for Model-Based Multiobject Tracking2023In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 59, no 6, p. 7363-7379Article in journal (Refereed)
    Abstract [en]

    Multiobject tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications, such as autonomous driving, tracking animal behavior, defense systems, and others. The MOT task can be divided into two settings, model based or model free, depending on whether accurate and tractable models of the environment are available. Model-based MOT has Bayes-optimal closed-form solutions, which can achieve state-of-the-art (SOTA) performance. However, these methods require approximations in challenging scenarios to remain tractable, which impairs their performance. Deep learning (DL) methods offer a promising alternative, but existing DL models are almost exclusively designed for a model-free setting and are not easily translated to the model-based setting. This article proposes a DL-based tracker specifically tailored to the model-based MOT setting and provides a thorough comparison to SOTA alternatives. We show that our DL-based tracker is able to match performance to the benchmarks in simple tracking tasks while outperforming the alternatives as the tasks become more challenging. These findings provide strong evidence of the applicability of DL also to the model-based setting, which we hope will foster further research in this direction.

  • 11.
    Saar de Moraes, Rodrigo
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Univ Fed Rio Grande do Sul, Brazil.
    de Freitas, Edison Pignaton
    Univ Fed Rio Grande do Sul, Brazil.
    Multi-UAV Based Crowd Monitoring System2020In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 56, no 2, p. 1332-1345Article in journal (Refereed)
    Abstract [en]

    This article presents the development of a multi-unmanned aerial vehicle (UAV) based crowd monitoring system, demonstrating a system that uses UAVs to periodically monitor a group of moving walking individuals. Using auction paradigms to distribute targets among UAVs and genetic algorithms to calculate the best order to visit the targets, the system has shown capabilities to efficiently perform the surveillance, visiting all the targets during a surveillance period and minimizing the time between the visits made to each target. Moreover, the system showed robustness keeping the good performance under a variety of situations.

  • 12.
    Sanchez, Miguel A.
    et al.
    Universidad Politecnica de Madrid, Madrid, Spain.
    Garrido, Mario
    Universidad Politecnica de Madrid, Madrid, Spain.
    Lopez-Vallejo, Marisa
    Universidad Politecnica de Madrid, Madrid, Spain.
    Grajal, Jesus
    Universidad Politecnica de Madrid, Madrid, Spain.
    Implementing FFT-based Digital Channelized Receivers on FPGA Platforms2008In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 44, no 4, p. 1567-1585Article in journal (Refereed)
    Abstract [en]

    This paper presents an in-depth study of the implementationand characterization of fast Fourier transform (FFT) pipelinedarchitectures suitable for broadband digital channelized receivers.When implementing the FFT algorithm on field-programmablegate array (FPGA) platforms, the primary goal is to maximizethroughput and minimize area. Feedback and feedforwardarchitectures have been analyzed regarding key designparameters: radix, bitwidth, number of points and stage scaling.Moreover, a simplification of the FFT algorithm, the monobitFFT, has been implemented in order to achieve faster real timeperformance in broadband digital receivers. The influence ofthe hardware implementation on the performance of digitalchannelized receivers has been analyzed in depth, revealinginteresting implementation trade-offs which should be taken intoaccount when designing this kind of signal processing systems onFPGA platforms.

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  • 13.
    Sjanic, Zoran
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    Gustafsson, Fredrik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    Navigation and SAR focusing with Map Aiding2015In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 51, no 3, p. 1652-1663Article in journal (Refereed)
    Abstract [en]

    A method for fusing Synthetic Aperture Radar (SAR) images with opticalaerial images is presented. This is done in a navigation framework, where the absolute position and orientation of the flying platform, as computed from the inertial navigation system, is corrected based on the aerial image coordinates taken as ground truth. The method is suitable for new low-price SAR systems for small unmanned vehicles. The primary application is remote sensing, where the SAR image provides one further "colour" channel revealing reflectivity to radio waves. The method is based on first applying an edge detection algorithm to the images and then optimising the most important navigation states by matching the two binary images. To get a measure of the estimation uncertainty, we embed the optimisation in a least squares framework, where an explicit method to estimate the (relative) size of the errors is presented. The performance is demonstrated on real SAR and aerial images, leading to an error of only a few pixels.

  • 14.
    Sjanic, Zoran
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Simultaneous Navigation and Synthetic Aperture Radar Focusing2015In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 51, no 2, p. 1253-1266Article in journal (Refereed)
    Abstract [en]

    Synthetic aperture radar (SAR) equipment is a radar imaging system that can be used to create high-resolution images of a scene by utilizing the movement of a flying platform. Knowledge of the platforms trajectory is essential to get good and focused images. An emerging application field is real-time SAR imaging using small and cheap platforms where estimation errors in navigation systems imply unfocused images. This contribution investigates a joint estimation of the trajectory and SAR image. Starting with a nominal trajectory, we successively improve the image by optimizing a focus measure and updating the trajectory accordingly. The method is illustrated using simulations using typical navigation performance of an unmanned aerial vehicle. One real data set is used to show feasibility, where the result indicates that, in particular, the azimuth position error is decreased as the image focus is iteratively improved.

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  • 15.
    Sjanic, Zoran
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    Skoglund, Martin A.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    Gustafsson, Fredrik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    EM-SLAM with Inertial/Visual Applications2017In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 53, no 1, p. 273-285Article in journal (Refereed)
    Abstract [en]

    The general Simultaneous Localisation and Mapping (SLAM) problem aims at estimating the state of a moving platform simultaneously with building a map of the local environment. There are essentially three classes of algorithms. EKF- SLAM and FastSLAM solve the problem on-line, while Nonlinear Least Squares (NLS) is a batch method. All of them scales badly with either the state dimension, the map dimension or the batch length. We investigate the EM algorithm for solving a generalized version of the NLS problem. This EM-SLAM algorithm solves two simpler problems iteratively, hence it scales much better with dimensions. The iterations switch between state estimation, where we propose an Extended Rauch-Tung-Striebel smoother, and map estimation, where a quasi-Newton method is suggested. The proposed method is evaluated in real experiments and also in simulations on a platform with a monocular camera attached to an inertial measurement unit. It is demonstrated to produce lower RMSE than with a standard Levenberg-Marquardt solver of NLS problem, at a computational cost that increases considerably slower. 

  • 16. Ulander, L.M.H.
    et al.
    Hellsten, H.
    Ericsson Microwave Systems, Sweden.
    Stenstrom, G.
    Synthetic-aperture radar processing using fast factorized back-projection2003In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 39, no 3, p. 760-776Article in journal (Refereed)
    Abstract [en]

    Exact synthetic aperture radar (SAR) inversion for a linear aperture may be obtained using fast transform techniques. Alternatively, back-projection integration in time domain can also be used. This technique has the benefit of handling a general aperture geometry. In the past, however, back-projection has seldom been used due to heavy computational burden. We show that the back-projection integral can be recursively partitioned and an effective algorithm constructed based on aperture factorization. By representing images in local polar coordinates it is shown that the number of operations is drastically reduced and can be made to approach that of fast transform algorithms. The algorithm is applied to data from the airborne ultra-wideband CARABAS SAR and shown to give a reduction in processing time of two to three orders of magnitude.

  • 17.
    Veibäck, Clas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Uncertain Timestamps in Linear State Estimation2019In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 55, no 3, p. 1334-1346Article in journal (Refereed)
    Abstract [en]

    We consider a linear state estimation problem, where, in addition to the usual timestamped measurements, observations with uncertain timestamps are available. Such observations could, e.g., come from traces left by a target in a tracking scenario or from witnesses of an event and have the potential to improve the estimation accuracy significantly. We derive the posterior distribution and point estimators for a linear Gaussian smoothing formulation of this problem and illustrate with two numerical examples.

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    Uncertain Timestamps in Linear State Estimation
  • 18.
    Veibäck, Clas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Olofsson, Jonatan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Lauknes, Tom Rune
    Norwegian Res Ctr, Norway.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Learning Target Dynamics While Tracking Using Gaussian Processes2020In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 56, no 4, p. 2591-2602Article in journal (Refereed)
    Abstract [en]

    Tracked targets often exhibit common behaviors due to influences from the surrounding environment, such as wind or obstacles, which are usually modeled as noise. Here, these influences are modeled using sparse Gaussian processes that are learned online together with the state inference using an extended Kalman filter. The method can also be applied to time-varying influences and identify simple dynamic systems. The method is evaluated with promising results in a simulation and a real-world application.

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  • 19.
    Zhang, Kewei
    et al.
    KTH Royal Inst Technol, Sweden.
    Larsson, Erik G
    Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
    Papadimitratos, Panos
    KTH Royal Inst Technol, Sweden.
    Protecting GNSS Open Service Navigation Message Authentication Against Distance-Decreasing Attacks2022In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 58, no 2, p. 1224-1240Article in journal (Refereed)
    Abstract [en]

    As the security of global navigation satellite systems (GNSSs) for civilian usage is increasingly important, navigation message authentication (NMA) significantly improves resilience to spoofing attacks. However, not all attacks can be effectively countered: a strong variant of replay/relay attacks, distance-decreasing (DD) attacks, can shorten pseudorange measurements, without manipulating the cryptographically protected navigation message, thus manipulating the position, velocity, and time solution undetected. First, we discuss how DD attacks can tamper with GNSS signals, demonstrating the attack effectiveness on a recorded Galileo signal. DD attacks might introduce bit errors to the forged signals, but the adversary can keep this error rate very low with proper attack parameter settings. Then, based on our mathematical model of the prompt correlator output of the tracking phase at the victim receiver, we find that the correlator output distribution changes in the presence of DD attacks. This leads us to apply hypothesis testing to detect DD attacks, notably a goodness-of-fit (GoF) test and a generalized likelihood ratio test (GLRT), depending on the victims knowledge on the DD attacks. Monte Carlo simulations are used to evaluate the detection probability and the receiver operating characteristic curves for two tests, for different adversary configuration and noise settings. Then, we evaluate the effectiveness of the GoF test and the GLRT with a synthesized DD signal. Both tests can detect DD attacks with similar performance in high-signal-to-noise-ratio (SNR) environments. The GLRT detection probability is approximately 20% higher than that of the GoF test in low-SNR environments.

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