Chosen results of field tests of synthetic aperture radar system installed on board UAV

Kaniewski, Piotr; Komorniczak, W.; Lesnik, Czeslaw; Cyrek, Jacek; Serafin, Piotr; Labowski, Michal; Wajszczyk, Bronisław

doi:10.1117/12.2269101

Cited by 4 publications

(6 citation statements)

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“…Detailed results of those tests were presented in [17,20,21,22,23]. Apart from the online data processing, which was realized in our system, both radar and navigation data were registered for the post-mission offline processing.…”

Section: Results Of Ins/gps System Testingmentioning

confidence: 99%

Motion Compensation for Radar Terrain Imaging Based on INS/GPS System

Labowski

Kaniewski

Serafin

2019

Sensors

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In order to obtain good quality radar terrain images using an aerial-based synthetic aperture radar, a motion compensation procedure must be applied. This procedure can use a precise navigation system in order to determine the aircraft’s position and velocity. A major challenge is to design a motion compensation procedure that can operate in real time, which is crucial to ensure convenient data for a human analyst. The article discusses a possibility of Inertial Measurement System (INS)/Global Positioning System (GPS) navigation system usage in such a radar imaging system. A Kalman filter algorithm designed for this system is described herein, and its modifications introduced by the authors allow the use of navigational data not aligned in time and captured with different frequencies. The presented navigation system was tested using measured data. Radar images obtained with the INS/GPS-based motion compensation system were compared to the INS-only results and images obtained without navigation corrections. The evaluation results presented in the paper show that the INS/GPS system allows for better reduction of geometric distortions in images compared to the INS-based approach, which makes it more suitable for typical applications.

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Section: Results Of Ins/gps System Testingmentioning

confidence: 99%

Motion Compensation for Radar Terrain Imaging Based on INS/GPS System

Labowski

Kaniewski

Serafin

2019

Sensors

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“…13 was obtained with the 15.9 GHz sensor and computed with the modified RDA in the off-line mode. The presented image differs from the one presented in [7] as it was taken from a relatively high altitude of over 400 m and includes a larger urban area. Most other experiments were carried out during flights at altitudes between 150 and 300 m. Due to the greater distance to the observed area, the image synthesis algorithm required a longer synthetic aperture to maintain the assumed resolution.…”

Section: Radio Link and Communication Systemmentioning

confidence: 96%

“…where: k is the Boltzmann constant, T A is the antenna noise temperature, F LNA is the noise figure of the microwave amplifier, and L is the antenna line losses. Substituting (7) and ( 8) into (1), the formulae can be obtained for SNR OUT min and SNR OUT max , corresponding to R s max and R s min , respectively, which are the distances to the closest and farthest objects illuminated by the antenna main beam, respectively:…”

Section: Radar Sensors and Antenna Systemmentioning

confidence: 99%

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Metrology and Measurement Systems

Kaniewski¹,

Komorniczak²,

Lesnik³

et al. 2019

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This paper describes a synthetic aperture radar system for tactical-level imagery intelligence installed on board an unmanned aerial vehicle. Selected results of its tests are provided. The system contains interchangeable S-band and Ku-band linear frequency-modulated, continuous wave radar sensors that were built within a frame of a research project named WATSAR, conducted by the Military University of Technology and WB Electronics S.A. One of several algorithms of radar image synthesis, implemented in the scope of the project, is described in this paper. The WATSAR system can create online and off-line radar images.

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“…Therefore, objects detection and classification turn out to be more challenging for SAR images. Meanwhile, with the rapid increase of spaceborne SAR systems [2], airborne SAR systems [3], and unmanned aerial vehicle (UAV) SAR [4] systems, the number of high-resolution SAR images is also growing and becoming more popular, which has brought more opportunities for the widespread application of SAR imaging.…”

Section: Introductionmentioning

confidence: 99%

A New Deep Learning Network for Automatic Bridge Detection from SAR Images Based on Balanced and Attention Mechanism

et al. 2020

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Bridge detection from Synthetic Aperture Radar (SAR) images has very important strategic significance and practical value, but there are still many challenges in end-to-end bridge detection. In this paper, a new deep learning-based network is proposed to identify bridges from SAR images, namely, multi-resolution attention and balance network (MABN). It mainly includes three parts, the attention and balanced feature pyramid (ABFP) network, the region proposal network (RPN), and the classification and regression. First, the ABFP network extracts various features from SAR images, which integrates the ResNeXt backbone network, balanced feature pyramid, and the attention mechanism. Second, extracted features are used by RPN to generate candidate boxes of different resolutions and fused. Furthermore, the candidate boxes are combined with the features extracted by the ABFP network through the region of interest (ROI) pooling strategy. Finally, the detection results of the bridges are produced by the classification and regression module. In addition, intersection over union (IOU) balanced sampling and balanced L1 loss functions are introduced for optimal training of the classification and regression network. In the experiment, TerraSAR data with 3-m resolution and Gaofen-3 data with 1-m resolution are used, and the results are compared with faster R-CNN and SSD. The proposed network has achieved the highest detection precision (P) and average precision (AP) among the three networks, as 0.877 and 0.896, respectively, with the recall rate (RR) as 0.917. Compared with the other two networks, the false alarm targets and missed targets of the proposed network in this paper are greatly reduced, so the precision is greatly improved.

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Chosen results of field tests of synthetic aperture radar system installed on board UAV

Cited by 4 publications

References 3 publications

Motion Compensation for Radar Terrain Imaging Based on INS/GPS System

Motion Compensation for Radar Terrain Imaging Based on INS/GPS System

Metrology and Measurement Systems

A New Deep Learning Network for Automatic Bridge Detection from SAR Images Based on Balanced and Attention Mechanism

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