SAR imaging via efficient implementations of sparse ML approaches

Glentis, George-Othon; Zhao, Kun; Jakobsson, Andreas; Abeida, Habti; Li, Jian

doi:10.1016/j.sigpro.2013.08.003

Cited by 21 publications

(3 citation statements)

References 34 publications

(58 reference statements)

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“…However, the contribution of this phenomenon to estimation problems has received scant attention.In this paper we highlight this feature and show that it is a critical resource primarily for resolution problems, and can improve precision by orders of magnitude. Resolution problems are ubiquitous and highly important in science [5][6][7][8][9][10][11][12][13], and roughly speaking are characterized by vanishing distinguishably; i.e, the sensitivity to the seperation between two close objects or frequencies vanishes as these get close enough. This effect usually results in divergent uncertainty, leading to a resolution limit.…”

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confidence: 99%

Overcoming resolution limits with quantum sensing

2019

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The field of quantum sensing explores the use of quantum phenomena to measure a broad range of physical quantities, of both static and time-dependent types. While for static signals the main figure of merit is sensitivity, for time dependent signals it is spectral resolution, i.e. the ability to resolve two different frequencies. Here we study this problem, and develop new superresolution methods that rely on quantum features. We first formulate a general criterion for superresolution in quantum problems. Inspired by this, we show that quantum detectors can resolve two frequencies from incoherent segments of the signal, irrespective of their separation, in contrast to what is known about classical detection schemes. The main idea behind these methods is to overcome the vanishing distinguishability in resolution problems by nullifying the projection noise.

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confidence: 99%

Overcoming resolution limits with quantum sensing

2019

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“…Synthetic aperture radar (SAR) raw signal generation and image simulation [1,2] play a significant role in the development of SAR system. On the one hand, SAR raw signal generation is an effective and financial tool that enables us to obtain the echo data needed for the validation of the radar imaging algorithms; on the other hand, SAR image simulation provides a feasible way to establish a target feature database, which is the foundation of the target automatic recognition.…”

Section: Introductionmentioning

confidence: 99%

Missile-Borne SAR Raw Signal Simulation for Maneuvering Target

Xia

Huang

et al. 2016

International Journal of Antennas and Propagation

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SAR raw signal simulation under the case of maneuver and high-speed has been a challenging and urgent work recently. In this paper, a new method based on one-dimensional fast Fourier transform (1DFFT) algorithm is presented for raw signal simulation of maneuvering target for missile-borne SAR. Firstly, SAR time-domain raw signal model is given and an effective Range Frequency Azimuth Time (RFAT) algorithm based on 1DFFT is derived. In this algorithm, the “Stop and Go” (SaG) model is adopted and the wide radar scattering characteristic of target is taken into account. Furthermore, the “Inner Pulse Motion” (IPM) model is employed to deal with high-speed case. This new RFAT method can handle the maneuvering cases, high-speed cases, and bistatic radar cases, which are all possible in the missile-borne SAR. Besides, this raw signal simulation adopts the electromagnetic scattering calculation so that we do not need a scattering rate distribution map as the simulation input. Thus, the multiple electromagnetic reflections can be considered. Simulation examples prove the effectiveness of our method.

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“…Traditional synthetic aperture radar (SAR) systems can reconstruct two-dimensional images of the investigated area with weather independence and all-day operation capabilities [1]. However two-dimensional images could not meet the requirements in many applications.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional imaging algorithm for tomography SAR based on multiple signal classification

Ren

Qin

Tian

2014

2014 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

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The height imaging quality of tomography synthetic aperture radar obtained by the classic Fourier transform method is limited by the resolution and sidelobe problems. The spatial spectrum estimation methods can obtain lower sidelobe and narrower spectrum, and can obtain higher resolution in height direction with small number of baseline. In this paper, the multiple signal classification (MUSIC) algorithm is proposed for tomography synthetic aperture radar to obtain high quality image, and the estimated accuracy of the proposed method is investigated with different number of looks. The simulation results confirm the validity of the proposed method.Index Terms-synthetic aperture radar, three-dimensional imaging, tomography, spatial spectrum estimation, MUSIC.

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SAR imaging via efficient implementations of sparse ML approaches

Cited by 21 publications

References 34 publications

Overcoming resolution limits with quantum sensing

Overcoming resolution limits with quantum sensing

Missile-Borne SAR Raw Signal Simulation for Maneuvering Target

Three-dimensional imaging algorithm for tomography SAR based on multiple signal classification

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