Theoretical foundation of passive bistatic ISAR imaging

Giusti, Elisa

doi:10.1109/taes.2014.130181

Cited by 38 publications

(47 citation statements)

References 23 publications

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“…Following [29] and after the target's motion compensation, by assuming that the effective target rotation vector is constant within the observation time eff (n) eff and by choosing the Cartesian reference system T Z so that z 3 is aligned with eff , (3) can be rewritten as follows:…”

Section: Signal Modelmentioning

confidence: 99%

Compressive sensing–based algorithm for passive bistatic ISAR with DVB-T signals

Qiu

Giusti

Bacci

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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Inverse synthetic aperture radar (ISAR) images can be obtained using digital video broadcasting-terrestrial (DVB-T)-based passive radars. However, television broadcast-transmitted signals offer poor range resolution for imaging purposes, because they have a narrower bandwidth with respect to those transmitted by a dedicated ISAR system. To reach finer range resolutions, signals composed of multiple DVB-T channels are required. Problems arise, however, because DVB-T channels are typically widely separated in the frequency domain. The gaps between channels produce high grating Manuscript lobes in the image domain when Fourier-based algorithms are used to form the ISAR image. In this paper, compressive sensing theory is investigated to address this problem because of its ability to reconstruct sparse signals by using incomplete measures. By solving an optimization problem under the constraint of signal sparsity, passive ISAR images can be obtained with strongly reduced grating lobes. Both simulation and experimental results are shown to demonstrate the validity of the proposed approach.

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Section: Signal Modelmentioning

confidence: 99%

Compressive sensing–based algorithm for passive bistatic ISAR with DVB-T signals

Qiu

Giusti

Bacci

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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“…In particular, this paper focuses on target segmentation and classification using 2D ISAR range-crossrange images of passive radar systems [9]. The goal of the proposed method is to have a generic, model-free approach for image-based target recognition that can be used for various target classes and image resolutions, can be used with a low number of target samples, but can be easily extended to support larger target classes.…”

Section: Introductionmentioning

confidence: 99%

Automatic target classification in passive ISAR range-crossrange images

Manno-Kovacs

Giusti

Berizzi

et al. 2018

2018 IEEE Radar Conference (RadarConf18)

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Abstract-This paper presents a method for automatic analysis of passive radar 2D ISAR images to evaluate the possibilities and capabilities of image feature based target extraction and classification. The goal is to extend signal processing based detection and recognition methods with image information. The presented method is fast, easily embeddable and extendable, works near real-time, and we show its viability for classification using real passive 2D ISAR images.

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“…Some theoretical studies and experimental results have been published showing the target imaging capability of passive radars. Specifically, in [6, 7] a full theoretical foundation for passive bistatic inverse synthetic aperture radar (PB‐ISAR) is established. Experimental results of a civilian aircraft and a ship based on multiple‐channel digital television broadcasting (DVB‐T) signals prove the correctness of the theoretical framework.…”

Section: Introductionmentioning

confidence: 99%