Miranda35 Experiments in Preparation for Small Uav-Based Sar

Henke, Daniel; Caris, M.; Stanko, Stephan; Renker, Matthias; Wellig, Peter; Frioud, Max; Fagir, Julian; Guillaume, Sébastien; Meindl, Michael; Geiger, Alain; Sieger, Stefan; Janssen, Daniel; Klöppel, Frank

doi:10.1109/igarss.2019.8897902

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Miranda is a Ka band SAR imaging RADAR developed by Fraunhofer Institute for High Frequency Physics [31][32][33]. We worked with five images.…”

Section: Mirandamentioning

confidence: 99%

A Generic, Multimodal Geospatial Data Alignment System for Aerial Navigation

Martin-Lac,

Petit-Frere,

Le Caillec

2023

Remote Sensing

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We present a template matching algorithm based on local descriptors for aligning two geospatial products of different modalities with a large area asymmetry. Our system is generic with regards to the modalities of the geospatial products and is applicable to the self-localization of aerial devices such as drones and missiles. This algorithm consists in finding a superposition such that the average dissimilarity of the superposed points is minimal. The dissimilarity of two points belonging to two different geospatial products is the distance between their respective local descriptors. These local descriptors are learned. We performed experiments consisting in estimating a translation between optical (Pléiades) and SAR (Miranda) images onto vector data (OpenStreetMap), onto optical images (DOP) and onto SAR images (KOMPSAT-5). Each remote sensing image to be aligned covered 0.64 km2, and each reference geospatial product spanned over 225 km2. We conducted a total of 381 alignment experiments, with six unique modality combinations. In aggregate, the precision reached was finer than 10 m with 72% probability and finer than 20 m with 96% probability. This is considerably more than with traditional methods such as normalized cross-correlation and mutual information.

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“…Miranda is a Ka band SAR imaging RADAR developed by Fraunhofer Institute for High Frequency Physics [31][32][33]. We worked with five images.…”

Section: Mirandamentioning

confidence: 99%

A Generic, Multimodal Geospatial Data Alignment System for Aerial Navigation

Martin-Lac,

Petit-Frere,

Le Caillec

2023

Remote Sensing

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“…In 2005, Delft University of Technology developed an X-band SAR system, named TuDelft, mounted on a fixed-wing UAV [16], which solved the problems of continuous intra-pulse motion and frequency nonlinearity. Sandia Laboratory of the United States and the German European Aeronautic Defence and Space Company (EADS) have successively developed the Ka-band Mini-SAR and MiSAR [17], [18]. The quality of the system imaging results completely depends on the accuracy of the GNSS system [19].…”

Section: Introductionmentioning

confidence: 99%

“…Sandia Laboratory of the United States and the German European Aeronautic Defence and Space Company (EADS) have successively developed the Ka-band Mini-SAR and MiSAR [17], [18]. The quality of the system imaging results completely depends on the accuracy of the GNSS system [19]. Later, Germany's MIRANDA35 system [19] and SUMATRA system [20], China's NUAA-MiniSAR [10], IECAS-SAR [9] are the first to use auto-focusing algorithm, such as map-drift (MD) [21], phase gradient auto-focus (PGA) [22], [23], and minimum entropy auto-focus (MEA) [24], which does not rely on inertial navigation system (INS) for high-resolution imaging.…”

Section: Introductionmentioning

confidence: 99%

“…The quality of the system imaging results completely depends on the accuracy of the GNSS system [19]. Later, Germany's MIRANDA35 system [19] and SUMATRA system [20], China's NUAA-MiniSAR [10], IECAS-SAR [9] are the first to use auto-focusing algorithm, such as map-drift (MD) [21], phase gradient auto-focus (PGA) [22], [23], and minimum entropy auto-focus (MEA) [24], which does not rely on inertial navigation system (INS) for high-resolution imaging. These auto-focusing algorithms have the disadvantages of only estimating the second-order error, requiring isolated feature points, and high computational complexity [25]- [27].…”

Section: Introductionmentioning

confidence: 99%

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Demonstration of Simultaneous Localization and Imaging With Multirotor-Borne MiniSAR

Luomei

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper extends the segmental aperture imaging (SAI) algorithm [28] which tackles the challenges of synthetic aperture radar (SAR) imaging of miniaturized synthetic aperture radar (MiniSAR) onboard multi-rotor unmanned aerial vehicle (UAV). The SAI algorithm is developed based on an accurate phase error model of both translational and rotational motion of UAV. It does require auxiliary positioning data such as GPS/INS. We further extend it to simultaneously estimate and reconstruct the 3D trajectory of UAV from parameters derived from SAI motion compensation. Thus, it in fact accomplishes the simultaneously location and imaging (SLAI) using UAV-borne radar. It is first validated and evaluated via raw signal simulation using realistic trajectory data. For experiment purpose, a multirotor-borne MiniSAR system FUSAR-Ku is developed. Experimental results show that the proposed algorithm can achieve decimeter-resolution imaging performance as measured by various metrics, while simultaneously accomplishing centimeter-to-decimeter 3D self-positioning capability. It is a first demonstration of SLAI mode with a multirotor-borne MiniSAR.

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