Radar simulation of ship at sea using MOCEM V4 and comparison to acquisitions

Cochin, Christian; Louvigne, Jean-Christophe; Fabbri, R.; Barbu, Corentin Le; Knapskog, Atle Onar; Odegaard, Nina

doi:10.1109/radar.2014.7060241

Cited by 11 publications

(4 citation statements)

References 4 publications

(4 reference statements)

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“…13. Tab "results" of the PHYS_IQ tool by a set of scattering centers [17], and not only by a single scattering center with an RCS and a Swerling law. The backscattering from breaking waves is being improved in order to cope with observed space-time phenomena.…”

Section: Presentation Of Phys_iq Toolmentioning

confidence: 99%

A physical radar simulation tool of the sea clutter

Houssay¹,

Pinel²,

Hellouvry³

et al. 2014

2014 International Radar Conference

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This paper describes a fast simulation tool for computing a sea surface remotely sensed by radar, by using parallel computing on GPU cards. The electromagnetic backscattering model is based on the two-scale model, with the incorporation of shadowing and spike effects at low-grazing elevation angles. The contribution of breaking waves is analysed. This intuitive and interactive simulation tool offers the possibility to test a large range of environmental and radar configurations through the various functionalities of its graphical user interface.

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Section: Presentation Of Phys_iq Toolmentioning

confidence: 99%

A physical radar simulation tool of the sea clutter

Houssay¹,

Pinel²,

Hellouvry³

et al. 2014

2014 International Radar Conference

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“…2016, H. Hammer and al. 2009, Cochin et al 2014. such simulators often focus on detailed wave-target interactions on specific objects or are limited to smaller, less complex scenes.…”

Section: Introductionmentioning

confidence: 99%

Comparing Deep Learning Approaches for SAR Imaging: Electromagnetic and Segmentation-Driven Simulation versus Image-to-Image Style Transfer

Letheule,

Weissgerber,

Lobry

et al. 2024

Preprint

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This article explores two innovative approaches to simulating synthetic aperture radar (SAR) images from their optical modality equivalents using deep learning methods; one approach also incorporates a physical simulator. The goal is to generate realistic images of large scenes, which could be used for training supervised recognition algorithms or pilot training. We explore, on one hand, the use of conditional generative adversarial networks (cGAN) for supervised transfer from the optical to the radar modality. On the other hand, we use EMPRISE, a physical simulator developed by ONERA, which utilizes a description of materials present in the scene, and we consider achieving this description through supervised semantic segmentation of the optical image. The evaluation of the realism of the obtained SAR images focuses on the fidelity of textures, as well as the presence of bright scatterers. We propose to use the Bhattacharyya distance between the second and third log-cumulant diagrams of the real and simulated images to assess the fidelity of textures. We consider three complementary criteria for evaluating bright point similarity, in particular the chamfer distance between the point clouds detected in both real and simulated images. In our dataset, the cGAN model fails to generate bright points effectively, whereas EMPRISE generates a point cloud with an approximate chamfer distance of 15 pixels. Moreover, EMPRISE tends to overgenerate bright points in the simulated images but demonstrates superior performance in urban areas. Concerning textures, the Bhattacharyya distance for cGAN is approximately 1.72, indicating degraded performance over forested regions, whereas for EMPRISE, this distance is about 0.10, showing the best results in the same forest areas. The results demonstrate that while the cGAN approach produces images with realistic content, it falls short in accurately simulating micro-textures and bright scatterers. Despite being constrained by segmentation into a limited number of classes and exhibiting modest performance, the EMPRISE simulation proves to be more effective than cGAN at reproducing real SAR features.

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“…However, the lackness of measured data greatly limits its development, so it is necessary to describe the ship motions in detail for further studies by building motion model. A virtual model of ship is built in [7] and SAR image is obtained by the simulation device [8]. But there was no effective method to optimize imaging performance.…”

Section: Introductionmentioning

confidence: 99%

3-D MIMO Radar Imaging of Ship Target with Rotational Motions

Wang¹,

Huang²,

Hu³

2019

RADIOENGINEERING

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The problem of image defocusing and distortion occurs in synthetic aperture radar (SAR) imaging of ship target with rotations. Although many literatures have analyzed this problem, it cannot efficiently be solved due to the coherent accumulation time for SAR target imaging, which results from their working principle and mechanism. In this paper, three-dimensional MIMO radar imaging of ship target with rotations is proposed. The real-time advantage of MIMO radar imaging is utilized to achieve high-precision imaging of ship target with rotations. Firstly, the ship rotations characteristics and imaging distortion effects are analyzed in detail. Then, the three-dimensional MIMO radar echo model is built to correct the scattering positions in time, geometrical analysis is carried out to analyze and optimize the target scattering coefficient variation process. Finally, a sparse imaging algorithm based on maximum a posterior (MAP) method is proposed to obtain accurate imaging result. Theoretical analysis and simulation results show that the three-dimensional imaging of ship target based on MIMO radar has better imaging accuracy and real-time performance compared with SAR, and can effectively solve the problem of image defocusing and distortion.

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Radar simulation of ship at sea using MOCEM V4 and comparison to acquisitions

Cited by 11 publications

References 4 publications

A physical radar simulation tool of the sea clutter

A physical radar simulation tool of the sea clutter

Comparing Deep Learning Approaches for SAR Imaging: Electromagnetic and Segmentation-Driven Simulation versus Image-to-Image Style Transfer

3-D MIMO Radar Imaging of Ship Target with Rotational Motions

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