Potentials and limitations of SAR image simulators – A comparative study of three simulation approaches

Balz, Timo; Hammer, Horst; Auer, Stefan

doi:10.1016/j.isprsjprs.2014.12.008

Cited by 41 publications

(12 citation statements)

References 12 publications

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“…RaySAR focuses on local urban scenes imaged using very high-resolution SAR sensors and on the geometric correctness of the simulated signals; further, it has been made available to SAR community since 2016. It has some limitations including simplified reflection models, absence of a polarimetric aspect, and incompleteness of diffuse signal reflections, which make it unsuitable for target recognition [22], [24]. CohRaS® is a coherent SAR image simulator that uses a narrow-beam approximation; that is, it does not create raw data.…”

Section: Related Workmentioning

confidence: 99%

“…Further, it uses ray tracing, geometrical optics (GO), and physical optics (PO) to simulate the reflectivity maps of a 3D model by considering random scatterers. CohRaS® is used to capture many small images quickly, which is required for target classification purposes [23], [24]. SARViz is a real-time simulator that adopts rasterization implemented on graphics processing units, and this leads to very fast calculations with accuracy limitations because rasterization cannot appropriately manage multiple bounces [24], [25].…”

Section: Related Workmentioning

confidence: 99%

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SAR Image Generation of Ground Targets for Automatic Target Recognition Using Indirect Information

Yoo

Kim

2021

IEEE Access

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The effectiveness of using the simulated synthetic aperture radar (SAR) images of military targets in databases for automatic target recognition (ATR) is widely known. However, for simulated target images to be useful, they must be sufficiently similar to measured images; otherwise, they can degrade ATR performance. Two factors affect the quality of simulated SAR images: precision of the associated computer-aided design (CAD) model of the target and the accuracy and speed of the numerical techniques used to solve the electromagnetic problems in SAR image generation. In this study, a method for the 3D CAD modeling of the target is proposed; this method can be used when direct access to the target is not feasible and only indirect information is available. Further, a bistatic image formation concept based on the shooting-and-bouncing-ray technique is adopted; this concept helps achieve an accuracy comparable to that of the monostatic method. Moreover, it helps achieve a highly enhanced computation speed. In combination, these proposals provide an efficient and fast method to generate a database of simulated SAR images that can effectively support ATR activities. We demonstrate the effectiveness of the proposed method by comparing the simulated SAR images with the measured ones using structural similarity as a similarity measure; further, we evaluate the recognition rate obtained with the simulated images. We show that the used similarity measure bears a strong relation with the recognition rate, which is an aspect that may further contribute to considerable time savings when validating and refining simulated image databases.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SAR Image Generation of Ground Targets for Automatic Target Recognition Using Indirect Information

Yoo

Kim

2021

IEEE Access

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“…The origin of the rays is the center of the transmitting antennas and the rays are uniformly distributed around the line-of-sight directionl and within the beamwidth . It is worth recalling that the assumption of parallel rays [10,11] is not used in this work owing to short distances to travel when dealing with indoor applications. Objects in the scene are modeled by assigning specific parameters to the cells whose position in the grid corresponds to position of the objects.…”

Section: Dmentioning

confidence: 99%

“…RT algorithms have been exploited for analysis of several sensors, for example, laser [7], lidar [8,9], radar [10][11][12], and applications, for example, scene rendering and indoor wireless net design [13][14][15]. With specific reference to SAR imaging, it is worth noting that SAR simulators have been typically developed under the assumption of parallel rays [10,11], which is an adequate approximation for standard remote sensing applications. However, the assumption of parallel rays is, in general, not valid for indoor applications due to short distances to travel.…”

Section: Introductionmentioning

confidence: 99%

Indoor Operations by FMCW Millimeter Wave SAR Onboard Small UAS: A Simulation Approach

2016

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A dedicated system simulator is presented in this paper for indoor operations onboard small Unmanned Aerial Systems (UAS) by a novel millimeter wave radar sensor. The sensor relies on the principle of Synthetic Aperture Radar (SAR) applied to a Frequency Modulated Continuous Wave (FMCW) radar system. Input to the simulator are both design parameters for Synthetic Aperture Radar (SAR), which should be able to cope with the stringent requirements set by indoor operations, and information about platform navigation and observed scene. The scene generation task is described in detail. This is based on models for point target response on either a completely absorbing background or fluctuating background and ray tracing (RT) techniques. Results obtained from scene processing are finally discussed, giving further insights on expected results from high-resolution observation of an assigned control volume by this novel SAR sensor.

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“…According to [3, 4], existing radar data simulation can be divided into two categories: radar echo signal simulation and image simulation. The former type is based on different signal reflection models such as Kirchhoff physical optics (PO) [5], geometrical optics (GO) approximations [3], integral equation method (IEM) [6], and an approach related to the Phong shading model [7].…”

Section: Introductionmentioning

confidence: 99%

Radar data simulation using deep generative networks

Song

Wang

2019

J. eng.

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Potentials and limitations of SAR image simulators – A comparative study of three simulation approaches

Cited by 41 publications

References 12 publications

SAR Image Generation of Ground Targets for Automatic Target Recognition Using Indirect Information

SAR Image Generation of Ground Targets for Automatic Target Recognition Using Indirect Information

Indoor Operations by FMCW Millimeter Wave SAR Onboard Small UAS: A Simulation Approach

Radar data simulation using deep generative networks

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