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Статті в журналах з теми "SAR raw data compression"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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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 (January 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 (May 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 (November 10, 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 (June 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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Анотація:

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 after range focusing advised in corresponding reference. The algorithm proposed in this paper has a certain practical value.

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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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Анотація:

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 after range focusing advised in corresponding reference.

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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 (March 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 (August 27, 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 (April 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 (April 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 (March 11, 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 (December 2, 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 (April 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 (April 10, 2021): 1462. http://dx.doi.org/10.3390/rs13081462.

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Анотація:

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 algorithm is developed under the CFBP framework to deal with the motion error problem for missile-borne SAR application. In the algorithm, spectrum compression is implemented after a CFBP process, and the SAR images are transformed into the spectrum-compressed domain. Then, the analytical image spectrum is obtained by utilizing wavenumber decomposition based on which the property of motion induced error is carefully investigated. With the analytical image spectrum, it is revealed that the echoes from different scattering points are aligned in the same spectrum range and the phase error becomes a spatial invariant component after spectrum compression. Based on the spectrum-compressed domain, an effective and efficient data-driven MOCO algorithm is accordingly developed for accurate error estimation and compensation. Both simulations of missile-borne SAR and raw data experiment from maneuvering highly-squint airborne SAR are provided and analyzed, which show high focusing performance of the proposed algorithm.

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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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Анотація:

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 matrix as its recovery matrix firstly, and after reconstruction of the range direction the conventional azimuth compression with the use of matched filtering is carried out. The simulation results are given to prove the feasibility of the strategy. Compared to the conventional method, the proposed strategy has lower sidelobes in the range direction. Furthermore, the proposed method also possesses the anti-noise capability to certain extent.

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17

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 (March 2, 2011): 569–72. http://dx.doi.org/10.3724/sp.j.1146.2006.01285.

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18

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 (September 9, 2011): 1845–50. http://dx.doi.org/10.3724/sp.j.1146.2010.01394.

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19

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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20

Hu, Xianyang, Changzheng Ma, Ruizhi Hu, and Tat Yeo. "Imaging for Small UAV-Borne FMCW SAR." Sensors 19, no. 1 (December 27, 2018): 87. http://dx.doi.org/10.3390/s19010087.

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Анотація:

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 intra-pulse motion-induced effects with an additional spectrum scaling. After which, the modified range doppler algorithm is derived for second order range compression and block-wise range cell migration correction. Raw data-based motion compensation is carried out with a doppler tracker. Squinted azimuth dependent phase gradient algorithm is employed to deal with azimuth dependent parameters and inexact deramping, with minimum entropy-based autofocusing algorithms. Finally, azimuth nonlinear chirp scaling is used for azimuth compression. Simulation and real data experiment results presented verify the effectiveness of the above signal processing approach.

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21

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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Анотація:

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, and then the entire SAR image can be obtained by using FrFT with the optimum angles of the azimuth and range. Finally, the performances of the algorithms are assessed using simulated and real Radarsat-1 data sets. The results confirm that the FrFT-based missile-borne SAR processing methods provide enhanced resolution yielding both lower side lobes effects and improved target detection. The method introduced in this paper has important theoretical significance in detection and recognition for military targets and precision guidance.

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22

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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23

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 (September 28, 2021): 6487. http://dx.doi.org/10.3390/s21196487.

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Анотація:

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 this paper, the staring spotlight SAR with nonlinear frequency modulation (NLFM) signal and azimuth non-uniform sampling (ANUS) is proposed to obtain high geometric resolution SAR images with the low SLL and almost without any SNR reduction. The NLFM signal obtains non-equal interval frequency sampling points under uniform time sampling by adjusting the instantaneous chirp rate. Its corresponding PSD is similar to the weighting window function, and its pulse compression result without amplitude window weighting has low sidelobes. To obtain a similar Doppler frequency distribution for low sidelobe imaging in azimuth, the received SAR echoes are designed to be non-uniformly sampled in azimuth, in which the sampling sequence is dense in middle and sparse in both ends, and azimuth compression result with window weighting would also have low sidelobes. According to the echo model of the proposed imaging mode, both the back projection algorithm (BPA) and range migration algorithm (RMA) are modified and presented to handle the raw data of the proposed imaging mode. Both imaging results on simulated targets and experimental real SAR data processing results of a ground-based radar validate the proposed low sidelobe imaging mode.

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24

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 (August 2009): 1003–9. http://dx.doi.org/10.1016/j.pnsc.2008.11.007.

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25

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 (October 22, 2010): 2352–62. http://dx.doi.org/10.1007/s11432-010-4082-x.

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26

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 (June 21, 2017): 9. http://dx.doi.org/10.30536/j.ijreses.2017.v14.a2629.

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Анотація:

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 radar raw data by decreasing the sampling rate of ADC and to reduce the computational load by decreasing the dimension of the measurement matrix. The simulation results found that the reconstruction of SAR image using partial acquisition model has better resolution compared to the conventional method (Range Doppler Algorithm/RDA). On a target of a ship, that represents a low-level sparsity, a good reconstruction image could be achieved from a fewer number measurement. The study concludes that the method may speed up the computation time by a factor 4.49 times faster than with a full acquisition matrix.

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27

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

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Анотація:

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 is close to the theoretical Cramer–Rao lower bound under a realistic C-band Sentinel-1 decorrelation scenario. Both persistent and distributed scatterers (PSDS) are exploited in the ComSAR algorithm. The ComSAR performance is validated via simulation and application to Sentinel-1 data to map land subsidence of the salt mine Vauvert area, France. The proposed ComSAR yields consistently better performance when compared with the state-of-the-art PSDS technique. We make our PSDS and ComSAR algorithms as an open-source TomoSAR package. To make it more practical, we exploit other open-source projects so that people can apply our PSDS and ComSAR methods for an end-to-end processing chain. To our knowledge, TomoSAR is the first public domain tool available to jointly handle PS and DS targets.

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28

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 (October 19, 2019): 4549. http://dx.doi.org/10.3390/s19204549.

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Анотація:

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 observation matrix, which will entail huge computational and memory costs. In this paper, to enhance the computational efficiency, an efficient adaptive parameter estimation method for sparse SAR imaging is proposed. The complex image-based sparse SAR imaging method only considers the threshold operation of the complex image, which can reduce the computational costs significantly. By utilizing this feature, the parameter is pre-estimated based on a complex image. In order to estimate the sparsity accurately, adaptive parameter estimation is then processed in the raw data domain, combining with the pre-estimated parameter and azimuth-range decouple operators. The proposed method can reduce the computational complexity from a quadratic square order to a linear logarithm order, which can be used in the large-scale scene. Simulated and Gaofen-3 SAR data processing results demonstrate the validity of the proposed method.

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29

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 (October 1, 2020): 185. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp185-196.

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Анотація:

<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 computational operations. The computational complexity of the iterative algorithms is high due to updating operations of the measurement vector and large number of iterations that are used to reconstruct the images successfully. This paper presents a new adaptive OMP algorithm to overcome this issue by using certain threshold. The new adaptive OMP algorithm is compared with the classical OMP algorithm using the Receiver Operating Characteristic (ROC) curves. The MATLAB simulations show that the new adaptive OMP algorithm improves the probability of detection at lower SNRs, reduce the computational operations as well as the number of required iterations. FPGA implementation of both the classical OMP and the adaptive OMP algorithm are also presented in this paper.</span></p>

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30

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 (April 29, 2016): 855–63. http://dx.doi.org/10.1017/s1759078716000593.

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Анотація:

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 signal structure, 10 and 20 compressible Fourier coefficients are taken through CS to analyze the difference between them. In addition, we applied synthetic aperture radar (SAR) processing, also combined with SAR-Multiple Signal Classification over raw data. Experimental performance results are given and shown in the figures with high quality.

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31

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 (February 18, 2019): 653. http://dx.doi.org/10.3390/en12040653.

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Анотація:

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 smart meter data flow is established. On the user side, the SAEs are utilized to compress load data in a distributed way. Then, centralized classification is adopted at remote data center by softmax classifier. Through the layer-wise feature extracting of SAE, the sparse and lengthy raw data are expressed in compact forms and then classified based on features. A global fine-tuning strategy based on a well-defined labeled subset is embedded to improve the extracted features and the classification accuracy. Case studies in China and Ireland demonstrate that the proposed method is more capable to achieve the minimum of error and satisfactory compression ratios (CR) than benchmark compressors. It also significantly improves the classification accuracy on both appliance and house level datasets.

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32

Ząbkiewicz, Kamil. "Application of Normalized Compression Distance and Lempel-Ziv Jaccard Distance in Micro-electrode Signal Stream Classification for the Surgical Treatment of Parkinson’s Disease." Studies in Logic, Grammar and Rhetoric 56, no. 1 (December 1, 2018): 45–57. http://dx.doi.org/10.2478/slgr-2018-0040.

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Анотація:

Abstract Parkinson’s Disease can be treated with the use of microelectrode recording and stimulation. This paper presents a data stream classifier that analyses raw data from micro-electrodes and decides whether the measurements were taken from the subthalamic nucleus (STN) or not. The novelty of the proposed approach is based on the fact that distances based on raw data are used. Two distances are investigated in this paper, i.e. Normalized Compression Distance (NCD) and Lempel-Ziv Jaccard Distance (LZJD). No new features needed to be extracted due to the fact that in the case of high-dimensional data the process is extremely time-consuming. The k-nearest neighbour (k-NN) was chosen as the classifier due to its simplicity, which is essential in data stream classification. Results obtained from classifiers based on k-NN: k-NN, k-NN were compared with Probabilistic Approximate Window (k-NN with PAW); k-NN with Probabilistic Approximate Window and Adaptive Windowing (k-NN with PAW and ADWIN); and Self Adjusting Memory k-NN (SAM k-NN), which use the proposed distances, with the performance of the same classifiers but using standard Euclidean distance. Prequential accuracy was chosen as the performance measure. The results of the experiments performed with the described approach are in most cases better, i.e. the performance measures for kNN classifiers that use NCD and LZJD distances are better by up to 8.5 per cent and 14 per cent, respectively. Moreover, the proposed approach performs better when compared with other stream classification algorithms, i.e. Hoeffding Tree, Naive Bayes, and Leveraging Bagging. In the discussed case, an improvement of classification rate of up to 17.9 per cent when using Lempel-Ziv Jaccard Distance instead of the Euclidean was noted.

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33

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 (April 1, 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 while simultaneously measuring displacement, by compressing the skin between indenters (flat cylinders) of different diameters and a hard platform. SAIs were subcategorized according to whether their neural response saturated above or below 10 kPa compressive stress (SAI-H or SAI-L, respectively). Linear regression was used to evaluate the relationships between neuron response and stress and force and displacement. For all SAIs, the mean neural response was significantly and substantially more highly correlated with compressive stress than force or displacement. For the SAI-L subcategory, the mean correlation coefficient was significantly and substantially greater for stress than for force but not significantly different for displacement. The data from this study support the hypothesis that SAI mechanoreceptors stimulated by indentation encode compressive stress rather than force, displacement, or strain.

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34

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 (July 2009): 476–80. http://dx.doi.org/10.1109/lgrs.2009.2017498.

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35

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 (April 2002): 427–40. http://dx.doi.org/10.1109/taes.2002.1008977.

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36

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

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37

Qian, Yulei, and Daiyin Zhu. "High Resolution Imaging from Azimuth Missing SAR Raw Data via Segmented Recovery." Electronics 8, no. 3 (March 19, 2019): 336. http://dx.doi.org/10.3390/electronics8030336.

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Анотація:

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, the missing raw data is split into several parts in range direction and is recovered with a segmented recovery strategy. Then, the recovered raw data is available to be focused with traditional SAR imaging algorithms. The range migration algorithm is chosen to deal with the recovered raw data in this paper. Point target and area target simulations are carried out to validate the effectiveness of the proposed method on azimuth missing SAR raw data. Moreover, the proposed method is implemented on real SAR data in order to further provide convincing demonstration.

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38

Lee, Haemin, and Ki-Wan Kim. "An Integrated Raw Data Simulator for Airborne Spotlight ECCM SAR." Remote Sensing 14, no. 16 (August 11, 2022): 3897. http://dx.doi.org/10.3390/rs14163897.

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Анотація:

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 of the reflectivity map. The reflectivity map is generated by applying a noise-like speckle to an arbitrary grayscale optical image. The received jamming signals are generated by the jamming model, and their powers are determined by the jamming equivalent sigma zero (JESZ), a newly proposed quantitative measure for designing ECCM SAR systems. The phase errors due to the inaccuracy of the navigation system are also considered in the design of the proposed simulator, as navigation sensor errors were added in the motion measurement process, with the results used for the motion compensation. The validity and usefulness of the proposed simulator is verified through the simulation of autofocus algorithms, SAR jamming, and SAR ECCM with pulse diversity. Various types of autofocus algorithms were performed through the proposed simulator and, as a result, the performance trends were identified to be similar to those of the real data from actual flight tests. The simulation results of the SAR jamming and SAR ECCM indicate that the proposed JESZ is well-defined measure for quantifying the power requirements of ECCM SAR and SAR jammers.

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39

Qian, Yulei, and Daiyin Zhu. "Image Formation of Azimuth Periodically Gapped SAR Raw Data with Complex Deconvolution." Remote Sensing 11, no. 22 (November 18, 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. The proposed method mainly consists of phase compensation and recovering the azimuth spectrum of raw data with complex deconvolution. The gapped data become sparser in the range of the Doppler domain after phase compensation. Then, it is feasible to recover the azimuth spectrum of the complete data from gapped raw data via complex deconvolution in the Doppler domain. Afterwards, the traditional SAR imaging algorithm is capable of focusing the reconstructed raw data in this paper. The effectiveness of the proposed method was validated via point target simulation and surface target simulation. Moreover, real SAR data were utilized to further demonstrate the validity of the proposed method.

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40

Ikuma, Takeshi, Mort Naraghi-Pour, and Thomas Lewis. "Predictive Quantization of Range-Focused SAR Raw Data." IEEE Transactions on Geoscience and Remote Sensing 50, no. 4 (April 2012): 1340–48. http://dx.doi.org/10.1109/tgrs.2011.2167236.

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41

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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42

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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43

Guaragnella, Cataldo, and Tiziana D’Orazio. "A Data-Driven Approach to SAR Data-Focusing." Sensors 19, no. 7 (April 6, 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 parameters and the knowledge of all the specific geometric and radiometric information about the sensor might complicate the hardware and software requirements. Despite SAR data processing being a well-established imaging technique, the proposed algorithm aims to exploit the SAR coherent illumination, demonstrating the possibility of extracting the reference functions, both in range and azimuth directions, when a strong point scatterer (either natural or manmade) is present in the scene. The Singular Value Decomposition is used to exploit the inherent redundancy present in the raw data matrix, and phase unwrapping and polynomial fitting are used to reconstruct clean versions of the reference functions. Fairly focused images on both synthetic and real raw data matrices without the knowledge of mission parameters and ancillary data information can be obtained; as a byproduct, azimuth beam pattern and estimates of a few other parameters have been extracted from the raw data itself. In a previous paper, authors introduced a preliminary work dealing with this problem and able to obtain good-quality images, if compared to the standard processing techniques. In this work, the proposed technique is described, and performance parameters are extracted to compare the proposed approach to RD, showing good adherence of the focused images and pulse responses.

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44

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 raw data reconstruction. Echoes corresponding to different subpulses in the same subswath are separated by DBF with null steering onboard, while echoes received and stored by different azimuth channels are reconstructed by multiple Doppler reconstruction filters on the ground. Afterwards, the resulting MIMO-SAR raw data could be equivalent to the raw data of the single-channel burst mode, and classical burst mode imaging algorithms could be adopted to obtain final focused SAR images. Simulation results validate the proposed imaging approach.

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45

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 (November 30, 2022): 9328. http://dx.doi.org/10.3390/s22239328.

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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 the continuous splicing of azimuth data. The method mainly consists of four parts: de-skewing, resampling, azimuth continuous combination, and Doppler history recovery. After de-skewing, the raw data with different PRFs can be resampled individually to obtain a uniform azimuth sampling interval, and an appropriate azimuth time shift is introduced to ensure the continuous combination of the azimuth signal. Consequently, the resulting raw data are sufficiently and uniformly sampled in azimuth, which could be well handled by classical SAR-focusing algorithms. Simulation results on point targets validate the proposed azimuth pre-processing approach. Furthermore, compared with methods to process SAR data with continuous PRF, the proposed method is more effective.

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46

Xu, Zhilin, Bingchen Zhang, Hui Bi, Chenyang Wu, and Zhonghao Wei. "Comparison of Raw Data-Based and Complex Image-Based Sparse SAR Imaging Methods." Sensors 19, no. 2 (January 15, 2019): 320. http://dx.doi.org/10.3390/s19020320.

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Sparse signal processing has already been introduced to synthetic aperture radar (SAR), which shows potential in improving imaging performance based on raw data or a complex image. In this paper, the relationship between a raw data-based sparse SAR imaging method (RD-SIM) and a complex image-based sparse SAR imaging method (CI-SIM) is compared and analyzed in detail, which is important to select appropriate algorithms in different cases. It is found that they are equivalent when the raw data is fully sampled. Both of them can effectively suppress noise and sidelobes, and hence improve the image performance compared with a matched filtering (MF) method. In addition, the target-to-background ratio (TBR) or azimuth ambiguity-to-signal ratio (AASR) performance indicators of RD-SIM are superior to those of CI-SIM in down-sampling data-based imaging, nonuniform displace phase center sampling, and sparse SAR imaging model-based azimuth ambiguity suppression.

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47

Guo, Zhengwei, Zewen Fu, Jike Chang, Lin Wu, and Ning Li. "A Novel High-Squint Spotlight SAR Raw Data Simulation Scheme in 2-D Frequency Domain." Remote Sensing 14, no. 3 (January 29, 2022): 651. http://dx.doi.org/10.3390/rs14030651.

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Raw data simulation is the front-end work of synthetic aperture radar (SAR), which is of great significance. For high-squint spotlight SAR, the frequency domain simulation algorithm is invalid because of the range-azimuth coupling effect. In order to realize high-squint spotlight SAR raw data simulation in the frequency domain, an algorithm based on coordinate transformation and non-uniform fast Fourier transform (NUFFT) is proposed. This algorithm generates broadside raw data using a two-dimensional (2-D) frequency simulation algorithm; then, coordinate transformation is used by analyzing the characteristics of broadside and high-squint spotlight SAR. After coordinate transformation, NUFFT is carried out to realize the coupling relation in the 2-D frequency domain. Since the coordinate transformation ignores the influence of range walk, the range walk is compensated after NUFFT. As a result, compared with the traditional squint spotlight SAR frequency domain simulation algorithm, the proposed algorithm can improve the accuracy of point and distributed target imaging results, and the efficiency of the proposed algorithm can be significantly improved in contrast the traditional time domain algorithm.

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48

ELDHUSET, K. "Accurate attitude estimation using ERS-1 SAR raw data." International Journal of Remote Sensing 17, no. 14 (September 1996): 2827–44. http://dx.doi.org/10.1080/01431169608949109.

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49

Fornaro, G., E. Sansosti, R. Lanari, and M. Tesauro. "Role of processing geometry in SAR raw data focusing." IEEE Transactions on Aerospace and Electronic Systems 38, no. 2 (April 2002): 441–54. http://dx.doi.org/10.1109/taes.2002.1008978.

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50

Yang, Liang, Weidong Yu, Shichao Zheng, and Lei Zhang. "Efficient Bistatic SAR Raw Signal Simulator of Extended Scenes." International Journal of Antennas and Propagation 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/130784.

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Bistatic SAR system is a new mode that allocates the radar transmitter and receiver on different platforms and has more advantages compared to the monostatic case. However, the existing bistatic SAR raw data simulator in the frequency domain can only handle the case of translation invariant system. In this paper, an efficient 2D frequency-domain raw data simulator of extended scenes for bistatic SAR of translation variant system is proposed by a geometric transformation method for the first time, where inverse STOLT interpolation is used to formulate the range migration terms. The presented simulator can accommodate the translation variant bistatic SAR system compared with existing bistatic SAR simulator. And it is more efficient than the time domain one by making use of Fast Fourier Transform (FFT). Simulation results for point targets and a real SAR image demonstrate its validity and effectiveness.

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