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Journal articles on the topic 'SAR raw data compression'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Parkes, S. M., and H. L. Clifton. "The compression of raw SAR and SAR image data." International Journal of Remote Sensing 20, no. 18 (1999): 3563–81. http://dx.doi.org/10.1080/014311699211200.

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2

Pascazio, V., and G. Schirinzi. "SAR raw data compression by subband coding." IEEE Transactions on Geoscience and Remote Sensing 41, no. 5 (2003): 964–76. http://dx.doi.org/10.1109/tgrs.2003.811811.

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3

Moureaux, J. M., P. Gauthier, M. Barlaud, and P. Bellemain. "Raw SAR data compression using vector quantization." International Journal of Remote Sensing 16, no. 16 (1995): 3179–87. http://dx.doi.org/10.1080/01431169508954621.

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4

Hua, Bin, Haiming Qi, Ping Zhang, and Xin Li. "Vector quantization for saturated SAR raw data compression." Advances in Space Research 45, no. 11 (2010): 1330–37. http://dx.doi.org/10.1016/j.asr.2010.01.007.

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5

Zeng, Shang Chun, Yun Xia Xie, Yi Xian Chen, and Zhao Da Zhu. "Study on an Algorithm for SAR Raw Data Compression." Applied Mechanics and Materials 380-384 (August 2013): 1495–98. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1495.

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t is difficult to directly compress the raw data of synthetic aperture radar for its low relativity. In this paper, a new algorithm is put forward. Firstly range focusing is imposed to SAR raw data, which makes it have comparative high relativity, secondly a linear prediction is performed along the azimuth, lastly block adaptive quantization is used to the prediction difference series. The experiments manifest that with same bit rate, SQNR and SDNR of the algorithm proposed in this paper surpass that of BAQ algorithm. The calculation in this paper is far less than that of compression method af

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6

Zeng, Shang Chun, Xian Lin Deng, Yi Xian Chen, Yun Xia Xie, and Zhao Da Zhu. "A Compression Algorithm for SAR Data after Range Focusing." Advanced Materials Research 694-697 (May 2013): 2877–80. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2877.

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It is difficult to directly compress the raw data of synthetic aperture radar for its low relativity. In this paper, a new algorithm is put forward. Firstly range focusing is imposed to SAR raw data, which makes it have comparative high relativity, secondly a linear prediction is performed along the azimuth, lastly block adaptive quantization is used to the prediction difference series. The experiments manifest that with same bit rate, SQNR and SDNR of the algorithm proposed in this paper surpass that of BAQ algorithm. The calculation in this paper is far less than that of compression method a

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7

Pieterse, Chané, Warren P. Plessis, and Richard W. Focke. "Metrics to evaluate compression algorithms for raw SAR data." IET Radar, Sonar & Navigation 13, no. 3 (2019): 333–46. http://dx.doi.org/10.1049/iet-rsn.2018.5213.

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8

Qi, HaiMing, WeiDong Yu, and Xi Chen. "Piecewise linear mapping algorithm for SAR raw data compression." Science in China Series F: Information Sciences 51, no. 12 (2008): 2126–34. http://dx.doi.org/10.1007/s11432-008-0133-y.

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9

Minhui, Zhu, Peng Hailiang, Wu Yirong, and Qi Xuan. "Enhanced multistage vector quantization for SAR raw data compression." Journal of Electronics (China) 13, no. 2 (1996): 97–101. http://dx.doi.org/10.1007/bf02684748.

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10

Xuan, Qi, Zhu Minhui, and Peng Hailiang. "Rapid codebook search algorithm for SAR raw data compression." Journal of Electronics (China) 13, no. 2 (1996): 110–15. http://dx.doi.org/10.1007/bf02684750.

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11

Zeng, Shang-chun, and Zhao-da Zhu. "An Algorithm for SAR Raw Data Compression after Range Focusing." Journal of Electronics & Information Technology 30, no. 4 (2011): 921–24. http://dx.doi.org/10.3724/sp.j.1146.2006.01607.

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12

Qiu, Xiao-lan, Bin Lei, Yun-ping Ge, Dong-hui Hu, and Chi-biao Ding. "Performance Evaluation of Two Compression Methods for SAR Raw Data." Journal of Electronics & Information Technology 32, no. 9 (2010): 2268–72. http://dx.doi.org/10.3724/sp.j.1146.2009.01101.

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13

NARAGHI-POUR, Mort, Ricardo Cortez, and Takeshi Ikuma. "Analysis-by-synthesis compression of range-focused SAR raw data." IEEE Transactions on Aerospace and Electronic Systems 51, no. 2 (2015): 1298–309. http://dx.doi.org/10.1109/taes.2015.130788.

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14

Benz, U., K. Strodl, and A. Moreira. "A comparison of several algorithms for SAR raw data compression." IEEE Transactions on Geoscience and Remote Sensing 33, no. 5 (1995): 1266–76. http://dx.doi.org/10.1109/36.469491.

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15

Bao, Min, Song Zhou, and Mengdao Xing. "Processing Missile-Borne SAR Data by Using Cartesian Factorized Back Projection Algorithm Integrated with Data-Driven Motion Compensation." Remote Sensing 13, no. 8 (2021): 1462. http://dx.doi.org/10.3390/rs13081462.

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Due to the independence of azimuth-invariant assumption of an echo signal, time-domain algorithms have significant performance advantages for missile-borne synthetic aperture radar (SAR) focusing with curve moving trajectory. The Cartesian factorized back projection (CFBP) algorithm is a newly proposed fast time-domain implementation which can avoid massive interpolations to improve the computational efficiency. However, it is difficult to combine effective and efficient data-driven motion compensation (MOCO) for achieving high focusing performance. In this paper, a new data-driven MOCO algori

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16

Zhu, Zhi Zhen, Zhi Da Zhang, Fa Lin Liu, and Bin Bing Li. "An Anti-Noise Strategy of SAR Based on Compressive Sensing." Advanced Materials Research 403-408 (November 2011): 1937–40. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1937.

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In conventional synthetic aperture radar (SAR) systems, the resolution of SAR image is basically constrained by Nyquist sampling rate. It increases the requirement on A/D converter and the capacity of memories with higher resolution requirements. Compressive sensing (CS) is a possible solution to these problems. From the viewpoint of CS, sparse signals can be reconstructed from a small set of their linear measurements. In this paper, we proposed a strategy of SAR based on compressive sensing. Raw data from SAR are processed by the method of CS in the range direction with random convolution mat

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17

Sakr, M., A. S. Amein, F. M. Ahmed, G. M. Amer, and A. Youssef. "Enhanced range-doppler algorithm for SAR image formation." Journal of Physics: Conference Series 2616, no. 1 (2023): 012033. http://dx.doi.org/10.1088/1742-6596/2616/1/012033.

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Abstract A Synthetic Aperture Radar (SAR) system is a powerful source of information due to its capability to operate day and night and in all weather conditions. It is essential for military and civilian purposes. Pulse-focusing is employed by the SAR system for achieving both long-range detection and fine-range resolution. The Range-Doppler Algorithm (RDA) is the most popular pulse-focusing technique for creating images from coherent SAR data. Pulse compression techniques for SAR signal processing targets seek to improve map resolution, lower peak power, and increase the Signal-to-Noise Rati

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18

Zhang, Wen-chao, Yan-fei Wang, and Zhi-gang Pan. "SAR Raw Data Compression Based on 2D Real-Valued Discrete Gabor Transform." Journal of Electronics & Information Technology 30, no. 3 (2011): 569–72. http://dx.doi.org/10.3724/sp.j.1146.2006.01285.

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19

Eslam, Ashraf, A. M. Khalaf Ashraf, and M. Hassan Sara. "Real time FPGA implemnation of SAR radar reconstruction system based on adaptive OMP compressive sensing." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 185–96. https://doi.org/10.11591/ijeecs.v20.i1.pp185-196.

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Synthetic Aperture Radar (SAR) is an imaging system based on the processing of radar echoes. The produced images have a huge amount of data which will be stored onboard or transmitted as a digital signal to the ground station via downlink to be processed. Therefore, some methods of compression on the raw images provides an attractive option for SAR systems design. One of these techniques which used for image reconstruction is the Orthogonal Matching Pursuit (OMP). OMP is an iterative algorithm which need high computational operations. The computational complexity of the iterative algorithms is

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20

Li, Xin, Hai-ming Qi, Bin Hua, Hong Lei, and Wei-dong Yu. "Theoretical Analysis on Target Radiometric Error Resulting from Spaceborne SAR Raw Data Compression." Journal of Electronics & Information Technology 33, no. 8 (2011): 1845–50. http://dx.doi.org/10.3724/sp.j.1146.2010.01394.

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21

Jiang, Nan, Jiahua Zhu, Dong Feng, Zhuang Xie, Jian Wang, and Xiaotao Huang. "High-Resolution SAR Imaging with Azimuth Missing Data Based on Sub-Echo Segmentation and Reconstruction." Remote Sensing 15, no. 9 (2023): 2428. http://dx.doi.org/10.3390/rs15092428.

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Due to the substantial electromagnetic interference, radar interruptions, and other factors, the SAR system may fail to receive valid data in some azimuth areas. This phenomenon is known as Azimuth Missing Data (AMD). If classical SAR imaging algorithms are performed directly using AMD echo, the imaging results may be defocused or even display false targets, which seriously affects the accuracy of the image. Thus, we proposed a Sub-echo Segmentation and Reconstruction Azimuth Missing Data SAR Imaging Algorithm (SSR-AMDIA) to solve the problem of incomplete echo SAR imaging in this article. Ins

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22

Hu, Xianyang, Changzheng Ma, Ruizhi Hu, and Tat Yeo. "Imaging for Small UAV-Borne FMCW SAR." Sensors 19, no. 1 (2018): 87. http://dx.doi.org/10.3390/s19010087.

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Unmanned aerial vehicle borne frequency modulated continuous wave synthetic aperture radars are attracting more and more attention due to their low cost and flexible operation capacity, including the ability to capture images at different elevation angles for precise target identification. However, small unmanned aerial vehicles suffer from large trajectory deviation and severe range-azimuth coupling due to their simple navigational control and susceptibility to air turbulence. In this paper, we utilize the squint minimization technique to reduce this coupling while simultaneously eliminating

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23

D'Elia, C., G. Poggi, and L. Verdoliva. "Compression of SAR raw data through range focusing and variable-rate trellis-coded quantization." IEEE Transactions on Image Processing 10, no. 9 (2001): 1278–87. http://dx.doi.org/10.1109/83.941852.

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24

Chen, Yong, Hui Chang Zhao, Si Chen, and Shu Ning Zhang. "An Improved Focusing Algorithm for Missile-Borne SAR with High Squint." Applied Mechanics and Materials 608-609 (October 2014): 761–65. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.761.

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Due to that the Doppler parameters vary according to slant and the resolution is lower using imaging algorithm of traditional pulse compression in processing raw echo data of the missile-borne synthetic aperture radar (SAR). Moreover, an algorithm is proposed to solve these problems, which is based on the fractional Fourier transform (FrFT) for missile-borne SAR imaging. Firstly, an echo signal model is built for the terminal guidance stage of the missile-borne SAR. Secondly, measure the chirp rate of the echo signal through the local optimum processing and get the optimum angles for the FrFT,

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25

Xu, Wei, Lu Zhang, Chonghua Fang, Pingping Huang, Weixian Tan, and Yaolong Qi. "Staring Spotlight SAR with Nonlinear Frequency Modulation Signal and Azimuth Non-Uniform Sampling for Low Sidelobe Imaging." Sensors 21, no. 19 (2021): 6487. http://dx.doi.org/10.3390/s21196487.

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In synthetic aperture radar (SAR) imaging, geometric resolution, sidelobe level (SLL) and signal-to-noise ratio (SNR) are the most important parameters for measuring the SAR image quality. The staring spotlight mode continuously transmits signals to a fixed area by steering the azimuth beam to acquire azimuth high geometric resolution, and its two-dimensional (2D) impulse response with the low SLL is usually obtained from the 2D weighted power spectral density (PSD) by the selected weighting window function. However, this results in the SNR reduction due to 2D amplitude window weighting. In th

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26

Romano, Diego, Marco Lapegna, Valeria Mele, and Giuliano Laccetti. "Designing a GPU-parallel algorithm for raw SAR data compression: A focus on parallel performance estimation." Future Generation Computer Systems 112 (November 2020): 695–708. http://dx.doi.org/10.1016/j.future.2020.06.027.

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27

Qi, Haiming, and Weidong Yu. "Anti-saturation block adaptive quantization algorithm for SAR raw data compression over the whole set of saturation degrees." Progress in Natural Science 19, no. 8 (2009): 1003–9. http://dx.doi.org/10.1016/j.pnsc.2008.11.007.

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28

Arief, Rahmat. "A PARTIAL ACQUISITION TECHNIQUE OF SAR SYSTEM USING COMPRESSIVE SAMPLING METHOD." International Journal of Remote Sensing and Earth Sciences (IJReSES) 14, no. 1 (2017): 9. http://dx.doi.org/10.30536/j.ijreses.2017.v14.a2629.

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In line with the development of Synthetic Aperture Radar (SAR) technology, there is a serious problem when the SAR signal is acquired using high rate analog digital converter (ADC), that require large volumes data storage. The other problem on compressive sensing method,which frequently occurs, is a large measurement matrix that may cause intensive calculation. In this paper, a new approach was proposed, particularly on the partial acquisition technique of SAR system using compressive sampling method in both the azimuth and range direction. The main objectives of the study are to reduce the ra

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29

Wu, Shuya, Yunhua Wang, Qian Li, Yanmin Zhang, Yining Bai, and Honglei Zheng. "Simulation of Synthetic Aperture Radar Images for Ocean Ship Wakes." Remote Sensing 15, no. 23 (2023): 5521. http://dx.doi.org/10.3390/rs15235521.

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To assist in the detection of ship targets in complex sea conditions, a numerical simulation method is proposed to obtain synthetic aperture radar (SAR) images of time-varying ocean ship wakes under various radar, ship, and sea surface parameters. This method addresses the limitations of recent simulations, which failed to simultaneously incorporate different types of time-varying ship wakes, simulate based on the echo data, and discuss the velocity bunching (VB) effect on the image results. To address these issues, firstly, the time-varying wave height and velocity fields of the sea surface,

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30

Li, Xin, HaiMing Qi, Bin Hua, Hong Lei, and WeiDong Yu. "A study of spaceborne SAR raw data compression error based on a statistical model of quantization interval transfer probability." Science China Information Sciences 53, no. 11 (2010): 2352–62. http://dx.doi.org/10.1007/s11432-010-4082-x.

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31

Ho Tong Minh, Dinh, and Yen-Nhi Ngo. "Compressed SAR Interferometry in the Big Data Era." Remote Sensing 14, no. 2 (2022): 390. http://dx.doi.org/10.3390/rs14020390.

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Modern Synthetic Aperture Radar (SAR) missions provide an unprecedented massive interferometric SAR (InSAR) time series. The processing of the Big InSAR Data is challenging for long-term monitoring. Indeed, as most deformation phenomena develop slowly, a strategy of a processing scheme can be worked on reduced volume data sets. This paper introduces a novel ComSAR algorithm based on a compression technique for reducing computational efforts while maintaining the performance robustly. The algorithm divides the massive data into many mini-stacks and then compresses them. The compressed estimator

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32

Liu, Mingqian, Bingchen Zhang, Zhongqiu Xu, and Yirong Wu. "Efficient Parameter Estimation for Sparse SAR Imaging Based on Complex Image and Azimuth-Range Decouple." Sensors 19, no. 20 (2019): 4549. http://dx.doi.org/10.3390/s19204549.

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Sparse signal processing theory has been applied to synthetic aperture radar (SAR) imaging. In compressive sensing (CS), the sparsity is usually considered as a known parameter. However, it is unknown practically. For many functions of CS, we need to know this parameter. Therefore, the estimation of sparsity is crucial for sparse SAR imaging. The sparsity is determined by the size of regularization parameter. Several methods have been presented for automatically estimating the regularization parameter, and have been applied to sparse SAR imaging. However, these methods are deduced based on an

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33

Ashraf, Eslam, Ashraf A. M. Khalaf, and Sara M. Hassan. "Real time FPGA implemnation of SAR radar reconstruction system based on adaptive OMP compressive sensing." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 185. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp185-196.

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<p><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Synthetic Aperture Radar (SAR) is an imaging system based on the processing of radar echoes. The produced images have a huge amount of data which will be stored onboard or transmitted as a digital signal to the ground station via downlink to be processed. Therefore, some methods of compression on the raw images provides an attractive option for SAR systems design. One of these techniques which used for image reconstruction is the Orthogonal Matching Pursuit (OMP). OMP is an iterative algorithm which need high c

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34

Turk, Ahmet Serdar, Pinar Ozkan-Bakbak, Lutfiye Durak-Ata, Melek Orhan, and Mehmet Unal. "High-resolution signal processing techniques for through-the-wall imaging radar systems." International Journal of Microwave and Wireless Technologies 8, no. 6 (2016): 855–63. http://dx.doi.org/10.1017/s1759078716000593.

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Through-the-Wall Imaging is an ever-expanding area in which processing time, scanning time, vertical, and horizontal resolutions have been tried to improve. In this study, several methods are investigated to obtain efficient reconstruction of through-the-wall imaging radar signals with high resolution. Microwave radar signals, which are produced in YTU Microwave Laboratory, are processed by compressive sensing (CS). B and C scanned reflection data samples collected between 1 and 7 GHz frequency band are taken randomly at 1/4, 1/2 of whole amount and reconstructed by CS method. Considering the

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35

Huang, Xiaoyao, Tianbin Hu, Chengjin Ye, Guanhua Xu, Xiaojian Wang, and Liangjin Chen. "Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders." Energies 12, no. 4 (2019): 653. http://dx.doi.org/10.3390/en12040653.

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With the development of advanced metering infrastructure (AMI), electrical data are collected frequently by smart meters. Consequently, the load data volume and length increase dramatically, which aggravates the data storage and transmission burdens in smart grids. On the other hand, for event detection or market-based demand response applications, load service entities (LSEs) want smart meter readings to be classified in specific and meaningful types. Considering these challenges, a stacked auto-encoder (SAE)-based load data mining approach is proposed. First, an innovative framework for smar

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36

Ge, Weiqing, and Partap S. Khalsa. "Encoding of Compressive Stress During Indentation by Slowly Adapting Type I Mechanoreceptors in Rat Hairy Skin." Journal of Neurophysiology 87, no. 4 (2002): 1686–93. http://dx.doi.org/10.1152/jn.00414.2001.

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The mechanical state encoded by slowly adapting type 1 mechanoreceptors (SAI) during indentation was examined using an isolated preparation in a rat model. Skin and its intact innervation were harvested from the medial thigh of the rat hindlimb and placed in a dish, with the corium side down, containing synthetic interstitial fluid. The margins of the skin were coupled to an apparatus that could stretch and apply compression to the skin. Using a standard teased nerve preparation, the neural responses of single SAIs were identified. SAIs were stimulated, using controlled compressive stress whil

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37

Wentao An, Yi Cui, Weijie Zhang, and Jian Yang. "Data Compression for Multilook Polarimetric SAR Data." IEEE Geoscience and Remote Sensing Letters 6, no. 3 (2009): 476–80. http://dx.doi.org/10.1109/lgrs.2009.2017498.

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38

Jeong, H., J. H. Park, H. Y. Ryu, J. B. Kwon, and Y. Oh. "VLSI architecture for SAR data compression." IEEE Transactions on Aerospace and Electronic Systems 38, no. 2 (2002): 427–40. http://dx.doi.org/10.1109/taes.2002.1008977.

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39

Jancco-Chara, Jhohan, Facundo Palomino-Quispe, Roger Jesus Coaquira-Castillo, Julio Cesar Herrera-Levano, and Ruben Florez. "Doppler Factor in the Omega-k Algorithm for Pulsed and Continuous Wave Synthetic Aperture Radar Raw Data Processing." Applied Sciences 14, no. 1 (2023): 320. http://dx.doi.org/10.3390/app14010320.

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Synthetic aperture radar (SAR) raw data do not have a direct application; therefore, SAR raw signal processing algorithms are used to generate images that are used for various required applications. Currently, there are several algorithms focusing SAR raw data such as the range-Doppler algorithm, Chirp Scaling algorithm, and Omega-k algorithm, with these algorithms being the most used and traditional in SAR raw signal processing. The most prominent algorithm that operates in the frequency domain for focusing SAR raw data obtained by a synthetic aperture radar with large synthetic apertures is

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40

Qian, Yulei, and Daiyin Zhu. "High Resolution Imaging from Azimuth Missing SAR Raw Data via Segmented Recovery." Electronics 8, no. 3 (2019): 336. http://dx.doi.org/10.3390/electronics8030336.

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Synthetic Aperture Radar (SAR) raw data missing occurs when radar is interrupted by various influences. In order to cope with this problem, a new method is proposed to focus the azimuth missing SAR raw data via segmented recovery in this paper. A reference function in time domain is designed to make the missing raw data sparser in two dimensional frequency domain. Afterwards, greedy algorithms are available to recover the missing data in two dimensional frequency domain. In addition, in order to avoid range frequency aliasing problem caused by reference function multiplication in time domain,

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41

Qian, Yulei, and Daiyin Zhu. "Image Formation of Azimuth Periodically Gapped SAR Raw Data with Complex Deconvolution." Remote Sensing 11, no. 22 (2019): 2698. http://dx.doi.org/10.3390/rs11222698.

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The phenomenon of periodical gapping in Synthetic Aperture Radar (SAR), which is induced in various ways, creates challenges in focusing raw SAR data. To handle this problem, a novel method is proposed in this paper. Complex deconvolution is utilized to restore the azimuth spectrum of complete data from the gapped raw data in the proposed method. In other words, a new approach is provided by the proposed method to cope with periodically gapped raw SAR data via complex deconvolution. The proposed method provides a robust implementation of deconvolution for processing azimuth gapped raw data. Th

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42

Lee, Haemin, and Ki-Wan Kim. "An Integrated Raw Data Simulator for Airborne Spotlight ECCM SAR." Remote Sensing 14, no. 16 (2022): 3897. http://dx.doi.org/10.3390/rs14163897.

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Airborne synthetic aperture radar (SAR) systems often encounter the threats of interceptors or electronic countermeasures (ECM) and suffer from motion measurement errors. In order to design and analyze SAR systems while considering such threats and errors, an integrated raw data simulator is proposed for airborne spotlight electronic counter-countermeasure (ECCM) SAR. The raw data for reflected echo signals and jamming signals are generated in arbitrary waveform to achieve pulse diversity. The echo signals are simulated based on the scene model computed through the inverse polar reformatting o

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43

Li, Haisheng, Junshe An, Xiujie Jiang, and Meiyan Lin. "Raw Data Simulation of Spaceborne Synthetic Aperture Radar with Accurate Range Model." Remote Sensing 15, no. 11 (2023): 2705. http://dx.doi.org/10.3390/rs15112705.

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Simulated raw data have become an essential tool for testing and assessing system parameters and imaging performance due to the high cost and limited availability of real raw data from spaceborne synthetic aperture radar (SAR). However, with increasing resolution and higher orbit altitudes, existing simulation methods fail to generate SAR simulated raw data that closely resemble real raw data. This is due to approximations such as curved orbits, “stop-and-go” assumption, and Earth’s rotation, among other factors. To overcome these challenges, this paper presents an accurate range model with a

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44

Magli, E., and G. Olmo. "Lossy predictive coding of SAR raw data." IEEE Transactions on Geoscience and Remote Sensing 41, no. 5 (2003): 977–87. http://dx.doi.org/10.1109/tgrs.2003.811556.

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45

Mo, Hongbo, Wei Xu, and Zhimin Zeng. "Investigation on Beamspace Multiple-Input Multiple-Output Synthetic Aperture Radar Data Imaging." International Journal of Antennas and Propagation 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/2706836.

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The multiple-input multiple-output (MIMO) technique can improve the high-resolution wide-swath imaging capacity of synthetic aperture radar (SAR) systems. Beamspace MIMO-SAR utilizes multiple subpulses transmitted with different time delays by different transmit beams to obtain more spatial diversities based on the relationship between the time delay and the elevation angle in the side-looking radar imaging geometry. This paper presents a beamspace MIMO-SAR imaging approach, which takes advantage of real time digital beamforming (DBF) with null steering in elevation and azimuth multichannel ra

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46

Choi, Jihoon, and Wookyung Lee. "Drone SAR Image Compression Based on Block Adaptive Compressive Sensing." Remote Sensing 13, no. 19 (2021): 3947. http://dx.doi.org/10.3390/rs13193947.

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In this paper, an adaptive block compressive sensing (BCS) method is proposed for compression of synthetic aperture radar (SAR) images. The proposed method enhances the compression efficiency by dividing the magnitude of the entire SAR image into multiple blocks and subsampling individual blocks with different compression ratios depending on the sparsity of coefficients in the discrete wavelet transform domain. Especially, a new algorithm is devised that selects the best block measurement matrix from a predetermined codebook to reduce the side information about measurement matrices transferred

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47

Guaragnella, Cataldo, and Tiziana D’Orazio. "A Data-Driven Approach to SAR Data-Focusing." Sensors 19, no. 7 (2019): 1649. http://dx.doi.org/10.3390/s19071649.

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Synthetic Aperture RADAR (SAR) is a radar imaging technique in which the relative motion of the sensor is used to synthesize a very long antenna and obtain high spatial resolution. Several algorithms for SAR data-focusing are well established and used by space agencies. Such algorithms are model-based, i.e., the radiometric and geometric information about the specific sensor must be well known, together with the ancillary data information acquired on board the platform. In the development of low-cost and lightweight SAR sensors, to be used in several application fields, the precise mission par

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Werness, S. A., S. C. Wei, and R. Carpinella. "Experiments with wavelets for compression of SAR data." IEEE Transactions on Geoscience and Remote Sensing 32, no. 1 (1994): 197–201. http://dx.doi.org/10.1109/36.285202.

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Gleich, D., P. Planinsic, B. Gergic, and Z. Cucej. "Progressive space frequency quantization for SAR data compression." IEEE Transactions on Geoscience and Remote Sensing 40, no. 1 (2002): 3–10. http://dx.doi.org/10.1109/36.981344.

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Zhang, Zhuo, Wei Xu, Pingping Huang, Weixian Tan, Zhiqi Gao, and Yaolong Qi. "Azimuth Full-Aperture Processing of Spaceborne Squint SAR Data with Block Varying PRF." Sensors 22, no. 23 (2022): 9328. http://dx.doi.org/10.3390/s22239328.

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Abstract:

The block varying pulse repetition frequency (BV-PRF) scheme applied to spaceborne squint sliding-spotlight synthetic aperture radar (SAR) can resolve large-range cell migration (RCM) and reduce azimuth signal non-uniformity. However, in the BV-PRF scheme, different raw data blocks have different PRFs, and the raw data in each block are insufficiently sampled. To resolve the two problems, a novel azimuth full-aperture pre-processing method is proposed to handle the SAR raw data formed by the BV-PRF scheme. The key point of the approach is the resampling of block data with different PRFs and th

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