33 research outputs found

    ISAR Signal Formation and Image Reconstruction as Complex Spatial Transforms

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    SAR image reconstruction by expectation maximization based matching pursuit

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    Cataloged from PDF version of article.Synthetic Aperture Radar (SAR) provides high resolution images of terrain and target reflectivity. SAR systems are indispensable in many remote sensing applications. Phase errors due to uncompensated platform motion degrade resolution in reconstructed images. A multitude of autofocusing techniques has been proposed to estimate and correct phase errors in SAR images. Some autofocus techniques work as a post-processor on reconstructed images and some are integrated into the image reconstruction algorithms. Compressed Sensing (CS), as a relatively new theory, can be applied to sparse SAR image reconstruction especially in detection of strong targets. Autofocus can also be integrated into CS based SAR image reconstruction techniques. However, due to their high computational complexity, CS based techniques are not commonly used in practice. To improve efficiency of image reconstruction we propose a novel CS based SAR imaging technique which utilizes recently proposed Expectation Maximization based Matching Pursuit (EMMP) algorithm. EMMP algorithm is greedy and computationally less complex enabling fast SAR image reconstructions. The proposed EMMP based SAR image reconstruction technique also performs autofocus and image reconstruction simultaneously. Based on a variety of metrics, performance of the proposed EMMP based SAR image reconstruction technique is investigated. The obtained results show that the proposed technique provides high resolution images of sparse target scenes while performing highly accurate motion compensation. (C) 2014 Elsevier Inc. All rights reserved

    Ground-based ISAR imaging of cooperative and non-cooperative sea vessels with 3-D rotational motion

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    Includes bibliographical references (leaves 175-188).Inverse Synthetic Aperture Radar (ISAR) images of sea vessels are a rich source of information for radar cross section (RCS) measurement and ship classification. However, ISAR imaging of sea vessels is a challenging task because the 3-D rotational motion of such vessels often gives rise to blurring. Blurry ISAR images are not desirable because they lead to inaccurate parameter estimation, which reduces the probability of correct classification. The objective of this thesis is to explain how 3-D rotational motion causes blurring in ISAR imagery and to develop effective techniques for imaging cooperative and non-cooperative sea vessels for RCS measurement and ship-classification purposes respectively. Much research has been done to investigate the effect of 3-D rotational motion on an ISAR image under the assumption that an object's axis of rotation is constant over the coherent processing interval (CPI). In this thesis, a new quaternion-based system model is proposed to characterise the amount of blurring in an ISAR image when a sea vessel possesses 3-D rotational motion over a CPI. Simulations were done to characterise the migration of a scatterer through Doppler cells due to the time-varying nature of the Doppler generating axis of rotation. Simulation results with realistic 3-D rotational motion show substantial blurring in the cross-range dimension of the resulting ISAR image, and this blurring is attributed to the time-varying nature of the angle of the Doppler generating axis of rotation and the object's rotation rate over the CPI

    Decentralized approach for translational motion estimation with multistatic inverse synthetic aperture radar systems

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    This paper addresses the estimation of the target translational motion by using a multistatic Inverse Synthetic Aperture Radar (ISAR) system composed of an active radar sensor and multiple receiving-only devices. Particularly, a two-step decentralized technique is derived: the first step estimates specific signal parameters (i.e., Doppler frequency and Doppler rate) at the single-sensor level, while the second step exploits these estimated parameters to derive the target velocity and acceleration components. Specifically, the second step is organized in two stages: the former is for velocity estimation, while the latter is devoted to velocity estimation refinement if a constant velocity model motion can be regarded as acceptable, or to acceleration estimation if a constant velocity assumption does not apply. A proper decision criterion to select between the two motion models is also provided. A closed-form theoretical performance analysis is provided for the overall technique, which is then used to assess the achievable performance under different distributions of the radar sensors. Additionally, a comparison with a state-of-the-art centralized approach has been carried out considering computational burden and robustness. Finally, results obtained against experimental multisensory data are shown confirming the effectiveness of the proposed technique and supporting its practical application

    Computational Algorithms for Improved Synthetic Aperture Radar Image Focusing

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    High-resolution radar imaging is an area undergoing rapid technological and scientific development. Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) are imaging radars with an ever-increasing number of applications for both civilian and military users. The advancements in phased array radar and digital computing technologies move the trend of this technology towards higher spatial resolution and more advanced imaging modalities. Signal processing algorithm development plays a key role in making full use of these technological developments.In SAR and ISAR imaging, the image reconstruction process is based on using the relative motion between the radar and the scene. An important part of the signal processing chain is the estimation and compensation of this relative motion. The increased spatial resolution and number of receive channels cause the approximations used to derive conventional algorithms for image reconstruction and motion compensation to break down. This leads to limited applicability and performance limitations in non-ideal operating conditions.This thesis presents novel research in the areas of data-driven motion compensation and image reconstruction in non-cooperative ISAR and Multichannel Synthetic Aperture Radar (MSAR) imaging. To overcome the limitations of conventional algorithms, this thesis proposes novel algorithms leading to increased estimation performance and image quality. Because a real-time imaging capability is important in many applications, special emphasis is placed on the computational aspects of the algorithms.For non-cooperative ISAR imaging, the thesis proposes improvements to the range alignment, time window selection, autofocus, time-frequency-based image reconstruction and cross-range scaling procedures. These algorithms are combined into a computationally efficient non-cooperative ISAR imaging algorithm based on mathematical optimization. The improvements are experimentally validated to reduce the computational burden and significantly increase the image quality under complex target motion dynamics.Time domain algorithms offer a non-approximated and general way for image reconstruction in both ISAR and MSAR. Previously, their use has been limited by the available computing power. In this thesis, a contrast optimization approach for time domain ISAR imaging is proposed. The algorithm is demonstrated to produce improved imaging performance under the most challenging motion compensation scenarios. The thesis also presents fast time domain algorithms for MSAR. Numerical simulations confirm that the proposed algorithms offer a reasonable compromise between computational speed and image quality metrics

    Radar Imaging Based on IEEE 802.11ad Waveform in V2I Communications

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    Since most of vehicular radar systems are already exploiting millimeter-wave (mmWave) spectra, it would become much more feasible to implement a joint radar and communication system by extending communication frequencies into the mmWave band. In this paper, an IEEE 802.11ad waveform-based radar imaging technique is proposed for vehicular settings. A roadside unit (RSU) transmits the IEEE 802.11ad waveform to a vehicle for communications while the RSU also listens to the echoes of transmitted waveform to perform inverse synthetic aperture radar (ISAR) imaging. To obtain high-resolution images of the vehicle, the RSU needs to accurately estimate round-trip delays, Doppler shifts, and velocity of vehicle. The proposed ISAR imaging first estimates the round-trip delays using a good correlation property of Golay complementary sequences in the IEEE 802.11ad preamble. The Doppler shifts are then obtained using least square estimation from the echo signals and refined to compensate phase wrapping caused by phase rotation. The velocity of vehicle is determined using an equation of motion and the estimated Doppler shifts. Simulation results verify that the proposed technique is able to form high-resolution ISAR images from point scatterer models of realistic vehicular settings with different viewpoints. The proposed ISAR imaging technique can be used for various vehicular applications, e.g., traffic condition analyses or advanced collision warning systems

    Effects of Motion Measurement Errors on Radar Target Detection

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    This thesis investigates the relationships present between signal-to-clutter ratios, motion measurement errors, image quality metrics, and the task of target detection, in order to discover what factor merit greater focus in order to attain the highest probability of target detection success. This investigation is accomplished by running a high number of Monte Carlo trials through a coherent target detector and analyzing the results. The aforementioned relationships are demonstrated via sample synthetic aperture radar imagery, histograms, receiver operating characteristics curves, and error bar plots

    Digital Image Processing

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    This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

    ISAR Imaging per Sistemi Radar Bistatici

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    La presente tesi si inserisce all'interno di un'attività di ricerca svolta presso il Dipartimento di Ingegneria dell'Informazione e relativa allo sviluppo di tecniche e algoritmi utili ai fini di contribuire all'evoluzione del campo della sensoristica e del telerilevamento. L'argomento di tesi in particolare tratta l'applicazione della tecnica del RADAR ad Apertura Sintetica Inverso (ISAR) a sistemi RADAR di tipo bistatici (o più in generale multistatici) con una prima introduzione riguardo la tecnica ISAR standard applicata ai RADAR monostatici. Vengono quindi affrontati i temi relativi alla modellizzazione dei segnali che si ricevono e l'applicazione della tecnica ISAR standard a questi segnali, focalizzando poi l'attenzione alle distorsioni che si introducono rispetto al caso monostatico, introducendo quindi in concetto del "RADAR Equivalente Monostatico". Successivamente viene quindi descritto il simulatore da me realizzato in ambiente MatLab, in grado di simulare un qualunque scenario bistatico e i segnali ricevuti sia dal ricevitore che dal RADAR equivalente monostatico, con la successiva generazione delle due immagini, e illustrati alcuni esempi di simulazione particolarmente significativi, l'ultimo dei quali viene confrontato con dei dati reali forniti
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