The use of CLEAN processing for passive SAR image creation

Kulpa, Krzysztof; Samczyński, Piotr; Malanowski, Mateusz; Maślikowski, Łukasz; Kubica, Virginie

doi:10.1109/radar.2013.6586017

Cited by 20 publications

(17 citation statements)

References 12 publications

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“…From the equation above, we see that ∇g involves the adjoint of the forward operator F ij , which, in our implementation is approximated by the filtered-backprojection operator, K given in (27). The iteration in (29) is a projected gradient descent.…”

Section: B Optimization Methods For Image Reconstructionmentioning

confidence: 99%

“…Within this framework, a variety of passive SAR imaging applications have been studied [30]- [37]. In [33] experimental results are presented using Envisat-1 satellite as an illuminator of opportunity, imagery is formed using a method based on CLEAN processing [27]. To improve resolution, the use of the TerraSAR-X satellite as an illuminator of opportunity is studied in [30].…”

Section: B Related Workmentioning

confidence: 99%

“…In order to minimize the rank and ensure that (24) is equivalent to (25), the trace term in the objective function should dominate. To deal with this variation, we use an adaptive value of λ, and define it as where K is the matrix representation of the filteredbackprojection operator (27). A constant step size, α = 1e6 is used in all of our simulations.…”

Section: Computational Complexitymentioning

confidence: 99%

“…For all slow time samples s, the total computational complexity of this step is O(N 2 log N ).2) Form initial imageρ using filtered-backprojection: We form an initial image based on(27) using the filter Q i,j to compensate for the known amplitude terms. One can carry out this operation using backprojection method.Since the Kronecker scene, ρ is O(N 2 × N 2 ), the operation takes O(N 8 ) operations.…”

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confidence: 99%

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Passive Synthetic Aperture Radar Imaging Using Low-Rank Matrix Recovery Methods

Mason

Son

Yazıcı

2015

IEEE J. Sel. Top. Signal Process.

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We present a novel image formation method for passive synthetic aperture radar (SAR) imaging. The method is an alternative to widely used Time Difference of Arrival (TDOA) or correlation-based backprojection method. These methods backprojection work under the assumption that the scene is composed of a single or a few widely separated point targets.The new method overcomes this limitation and can reconstruct heterogeneous scenes with extended targets.We assume that the scene of interest is illuminated by a stationary transmitter of opportunity with known illumination direction, but unknown location. We consider two airborne receivers and correlate the fast-time bistatic measurements at each slow-time. This correlation process maps the tensor product of the scene reflectivity with itself to the correlated measurements. Since this tensor product is a rank-one positive semidefinite operator, the image formation lends itself to low-rank matrix recovery techniques. Taking into account additive noise in bistatic measurements, we formulate the estimation of the rank-one operator as a convex optimization with rank constrain. We present a gradient-descent based iterative reconstruction algorithm and analyze its computational complexity. Extensive numerical simulations show that the new method is superior to correlation-based backprojection in reconstructing extended and distributed targets with better geometric fidelity, sharper edges and better noise suppression.1932-4553 (c)

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Section: B Optimization Methods For Image Reconstructionmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: Computational Complexitymentioning

confidence: 99%

mentioning

confidence: 99%

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Passive Synthetic Aperture Radar Imaging Using Low-Rank Matrix Recovery Methods

Mason

Son

Yazıcı

2015

IEEE J. Sel. Top. Signal Process.

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“…Due to this isolation, which, in practice, is at the level of 20-30 dB, the direct signal to echo signal ratio in the surveillance channel is limited to 60-90 dB, which can be handled by modern AD converters. The remaining direct power, which can still mask the weak signals, can be further removed using CLEAN techniques [18], [19]. The application of this procedure reduces the direct signal by 20-60 dB, which significantly decreases the masking effect and improves the final passive SAR image.…”

Section: Concept Of Passive Sar Imagingmentioning

confidence: 99%

Experimental Results of Passive SAR Imaging Using DVB-T Illuminators of Opportunity

Gromek

Kulpa

Samczyński

2016

IEEE Geosci. Remote Sensing Lett.

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In this letter, pioneering experimental results of passive synthetic aperture radar (SAR) imaging are presented. The classical active SAR radar operates in monostatic geometry. The SAR sensor presented in this letter is a passive radar utilizing commercial Digital Video Broadcasting-Terrestrial transmitters as illuminators of opportunity. It works in a bistatic configuration, where the receiver is placed on a moving platform and the transmitter is placed on the ground and is stationary. The imaged scenes are stationary surfaces on Earth such as agriculture or urban areas, buildings, etc. In this letter, pioneering results of signal processing verified by a measurement campaign are presented. In the experiment, two synchronized passive radar receivers were mounted on a small airborne platform. The main goal of the presented experiment was to verify the possibility of ground imaging using passive SAR technology and validate previously presented theoretical results. Index Terms-Bistatic SAR (BSAR), Digital VideoBroadcasting-Terrestrial (DVB-T), passive radar, passive SAR, synthetic aperture radar (SAR).

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Deep learning for waveform estimation and imaging in passive radar

Yonel

Mason

Yazıcı

2019

IET Radar, Sonar & Navigation

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We consider a bistatic configuration with a stationary transmitter transmitting unknown waveforms of opportunity and a moving receiver and present a Deep Learning (DL) framework for passive synthetic aperture radar (SAR) imaging. Existing passive radar methods require two or more antennas that are either spatially separated or colocated with sufficient directivity to estimate the underlying waveform prior to imaging. Our approach to passive radar only requires a single receiver, hence reducing cost and increasing versatility. We approach DL from an optimization perspective and formulate image reconstruction as a machine learning task. By unfolding the iterations of a proximal gradient descent algorithm, we construct a deep recurrent neural network (RNN) that is parameterized by transmitted waveforms. We cascade the RNN structure with a decoder stage to form a recurrent-auto encoder architecture. We then use backpropagation to learn transmitted waveforms by training the network in an unsupervised manner using SAR measurements. The highly non-convex problem of backpropagation is guided to a feasible solution over the parameter space by initializing the network with the known components of the SAR forward model. Moreover, prior information regarding the waveform structure is incorporated during initialization and backpropagation. We demonstrate the effectiveness of the DL-based approach through extensive numerical simulations that show focused, high contrast imagery using a single receiver antenna at realistic SNR levels.

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The use of CLEAN processing for passive SAR image creation

Cited by 20 publications

References 12 publications

Passive Synthetic Aperture Radar Imaging Using Low-Rank Matrix Recovery Methods

Passive Synthetic Aperture Radar Imaging Using Low-Rank Matrix Recovery Methods

Experimental Results of Passive SAR Imaging Using DVB-T Illuminators of Opportunity

Deep learning for waveform estimation and imaging in passive radar

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