Metrics to evaluate compression algorithms for raw SAR data

Pieterse, Chané; Plessis, Warren P. du; Focke, Richard W.

doi:10.1049/iet-rsn.2018.5213

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…Various radar electronic countermeasures (ECM) have been developed in recent decades to prevent the detection of sensitive targets and protected areas by enemy SAR [1,3,4]. SAR deception jamming has become popular in recent years because of its low power requirements, flexible operability, and significant damage to SAR [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Generation of a synthetic aperture radar deception jamming signal based on a deep echo inversion network

Xiao

Dai

et al. 2023

IET Radar Sonar & Navi

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Existing methods for generating synthetic aperture radar (SAR) deception jamming signals have slow speed, low imaging quality, and insufficient intelligence in complex electromagnetic environments. This paper proposes a deep learning-based SAR deception jamming signal generation method based on deep echo inversion Unet (DEIUnet). This method has high speed and provides high-image quality of the interference signal. A Swin Next (SN) block is proposed to combine local and non-local information in the image and echo data. The Unet structure consists of SN blocks, and a residual connection is used as the jump connection to fuse the multi-scale feature information from the echo and image data. PixelShuffle is utilised for up-sampling to generate high-quality echo data. The experimental results on MSTAR and Sentinel-1 data sets verify the effectiveness and superiority of DEIUnet for echo inversion. The imaging results of the SAR deception jamming signal generated by DEIUnet on an MSTAR scene confirm the effectiveness of the proposed method. K E Y W O R D S deception jamming signal generation, deep echo inversion unet (DEIUnet), synthetic aperture radar (SAR)This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Generation of a synthetic aperture radar deception jamming signal based on a deep echo inversion network

Xiao

Dai

et al. 2023

IET Radar Sonar & Navi

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“…Metrics to compare performance of compression algorithms [79] can be applied at the (i) data domain, which would primarily target data fidelity after decompression and (ii) image domain, which would deal with features of the image and its quality. The proposed method achieves a simultaneous decompression and decomposition of the compressed raw data and focuses on analysis of the image after SAR processing.…”

Section: Image Quality Analysismentioning

confidence: 99%

Sparse representation and its applications to airborne platforms

Srinivasan¹

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This thesis would not be what it is without the guidance, keen inputs and support of my supervisor, Prof Anamitra Makur. He has been ever willing to offer his help with suggestions and will always be someone I look up to for his calm demeanour and patience. I am eternally indebted to my co-supervisor, Dr. Asha Vijayakumar for being extremely supportive, kind and being there always to lift me up when my spirits fell. Conversations with her spanning the entire spectrum from technical discussions to light-hearted chats will always be my cherished memories. I have been fortunate to have been guided by the most patient and encouraging supervisors.I am extremely grateful to the Economic Development Board (EDB), Singapore for sponsoring this Industrial PhD Programme (IPP) and enabling me to work towards this. I am thankful to Airbus for having given me the opportunity to undertake this Industrial PhD Programme (IPP) with NTU. I am thankful to Cyrille, Francois and Julian at Airbus for their support and push to see this thesis through. Special thanks to Roger for having been the kind person that he is and being understanding of the time-sharing I needed to do for the research and the project work. Thanks to Supriti and Archana for the happy times at Airbus. I am thankful to Mukund, Sathiya and Sujit for the most interesting and helpful discussions on Compressed Sensing and more.My eternal pillars of support and strength, Porna, Puppy, Tree and Gee, thank you for always being there for me! Much love and thanks to my other family at Smule, Vimmo and i Nisha for being the stress-buster the last 3 years with their music and love! My dearest family, Appa, Amma, Sivaram, Sarada and the newest entrant, Ahaana -there is never a dull moment with all of you, thanks for all the love and the faith in me! No amount of thanks can suffice for Ravi and my darling daughter, Kavya for giving me the space and time to deal with the stress and the pressure to complete this. Special love to Kavya for having tolerated me these last few years while I was battling with work. Sometimes, all I needed was her smile at the end of the day to cheer me up! poses. Intrusion-detection schemes are of interest in every surveillance system. Mobile flying platforms such as drones are being used effectively as an autonomous surveillance system that can not only detect an intrusion on-board, but also make an informed decision to either track the intruder or transmit the required information to the ground station. Efficient techniques to implement a surveillance system on-board continue to be explored. A CS-based scheme for intrusion detection on-board the AR Parrot Drone is implemented. It is observed that a CS-based system can make efficient use of on-board resources by exploiting the properties of the compressed data space. It is also capable of efficient usage of the available bandwidth by transmitting frames to the ground station only if an intrusion is detected. Performance metrics, namely, encoding time and detection accuracy were evaluated. The detectio...

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