以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數：21 、訪客IP：44.200.140.218

姓名蔣政諺(Cheng-Yen Chiang) 查詢紙本館藏 畢業系所資訊工程學系 論文名稱基於GPU的SAR資料庫模擬器：SAR回波訊號與影像資料庫平行化架構 (PASSED)

(GPU Based SAR Database Simulator: Parallelization Architecture for Simulation of SAR Echo and Database (PASSED))檔案[Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] 至系統瀏覽論文 ( 永不開放) 摘要(中)為了清楚地了解合成孔徑雷達(Synthetic Aperture Radar, SAR)複雜的特性，特別是當近代的雷達系統逐漸走向高解析、多波段、全偏極 和混和多種觀測模式的系統下，需要萃取出更多目標物特徵來輔助判斷。此篇文章中的SAR影像資料庫模擬器能發揮以下多種功能。為了達到輔助目標物辨識的目的，多重角度的特性囊括於此模擬器中，利用此多重角度的資料庫以降低SAR目標影像的角度高靈敏問題，藉此提高辨識的精確度。此外此模擬器亦可用在SAR散射機制的了解上，藉由分離不同反射和尖端散射下的背向散射結果圖，得而清楚地了解何處的散射電場分布是肇因於何處複雜目標的結構上。透過這樣的各層次的背向散射結果圖，特別適合用在複雜目標模型上，得以區別哪些是因目標或背景的影響。在計算機輔助工程(Computer-aided engineering, CAE)的目的上，能透過前期的軟體模擬適當地降低實驗的風險，因此無論是在SAR處理中的演算法亦或是硬體階段的開發，皆可仰賴此模擬器的優勢。其他例如硬體模擬器的應用，亦可使用此模擬器提供後端的軟體資料產製，這樣的硬體模擬器主要是透過軟硬體整合以提供複雜的人造SAR訊號，再由硬體發射。

本篇文章中提供了完整的模擬器流程，由模擬參數表的產製至完整的多重角度SAR目標資料庫。有別於相關的文獻，此模擬器結合目標物與背景的交互關係，並著重在多重角度的SAR目標物影像產製，從而有相應的平行化處理需求與實作，亦使此模擬器更具真實和可行性。在實作此模擬器上運用了諸多技巧。在背向散射點場的計算上，同時考量了多次反射與尖端散射效應，相應而生的光線追跡法得以滿足多次反射的需求，物理光學法(Physical Optics, PO)以近似解表面電流，物理散射法(Physical Theory of Diffraction, PTD)得以求解楔型尖端散射。多層與不同材質的表面亦考慮於其中。為了求取高精度表面積分問題，文章中採用了泰勒展開式逼近法。綜合以上方法，可使背向散射電場不僅局限於單一反射面下的散射現象，而物理散射法特別適合利用在目標物高複雜度的邊緣結構。在斜距向的取樣點上，為減少計算複雜，所有計算上皆為頻率域的樣本點，藉此頻率樣本點數可限縮在感興趣的斜距跨幅內，實為相依於目標物的大小，而獨立於目標物的斜距距離。為求更真實，基於星載和機載的感測器軌跡模擬亦包含於此模擬器中，諸如感測器量測裝置上的偏移，抑或雜訊誤差等皆包含於其中。更重要的為，考慮多種SAR觀測模式，包含Circle SAR、Inverse SAR、Stripmap和Spotlight等模式，可將大部分現行運作中的SAR系統囊括於此模擬器中，相應而生的成像方法諸如Range-Doppler Algorithm (RDA)、Omega-K Algorithm (WKA) 和 Time-domain Back Proejction (TDBP)等，都需搭配在不一樣的觀測模式下。

綜觀整個模擬器，由於此計算量即便在單一目標下實為龐大，則在資料庫的產製上更為不可能，因此基於NVidia CUDA技術則用來加速整體模擬器。根據程式碼剖析，在背向散射電場計算是為主要最佳化平行的區塊，經過層層的加速測試，最終最佳化的平行化程式可將速度提昇至少200倍以上，而複雜目標物的組合三角片可多達百萬片，在大區域目標上經實測可達到250公尺 x 250 公尺的範圍，而單一角度下的影像可達到近乎30分鐘的計算時間，因此推論此模擬器即便在非大型工作站下及有其模擬的實用性。最後使用的MSTAR量測車輛資料模擬小範圍高解析度的複雜目標物影像，而英國白金漢宮模型資料則用於實現大範圍的場景目標，其中目標物的尺寸維度與散射特徵亦包含於測試結果中。摘要(英)In order to understand clearly the complicated characteristics of synthetic aperture radar (SAR) especially in high-resolution condition, multi-band, fully polarization and multi-acquisition mode, more and more features need to be extracted. For recognition or identification purpose, the multi-aspect angle database is essential because of the accuracy of classification. The high angle sensitivity of SAR target image was considered. For the SAR mechanism understanding, a distinction of the backscatter electric field between each reflection and diffraction is necessary. According to the separated scattering electric field map, which reflections are due to which structures of complex target can be clearly identified. This simulator is very suitable to understand these characteristics. For the computer-aided engineering (CAE) purpose that is adopted for the engineering analysis to avoid the risk of experimentation widely, computer software orientation simulation is the earliest motivation. For the evaluation of SAR algorithm or system this simulator can be adopted to be the first version before lunching or flight-testing. For the hardware orientation emulator application, a combination of software and hardware is very suitable for providing the man-made artificial SAR complex target’s signal.

An overall simulation flowchart from simulation parameter tables to SAR target’s database was introduced in this dissertation. Different from the literature are that the interaction between target and background was considered, the purpose of this simulator was multi-angle orientation, and the optimal parallelization architecture was introduced by power of CUDA that make this simulator be authentic and usable. To implement this simulator, a lot of technologies were considered. The simulation of the back scattering electric field must consider the multi-bouncing back scattering and diffraction. For the purpose of parallelization, the ray-tracing technology was adopted. After the ray tracing, the Physical Optics (PO) and Physical Theory of Diffraction (PTD) methods with multi-layer material was adopted. For more high accuracy consideration, the Taylor expansion method was used for solving the integration of surface current. Therefore, the back scattering electric filed is not restricted in the single bounce. Furthermore, an analytical diffraction solution was adopted for the wedge structure that is very common on complex targets in CAD model especially. For reducing the computation time, a slant range sample in frequency domain was adopted to limited the number of samples in the swath region. Furthermore, the sensor path trajectory for space- and air-borne was considered. For the authenticity, bias and noise can be generated in this simulator to meet the real sensor requirement. Because that this simulator is for producing appropriate SAR databases, the SAR geometry combination with different kinds of observation type was considered such as the Circle SAR, Inverse SAR, Stripmap and Spotlight. Various SAR focusing algorithms such as the Range-Doppler Algorithm (RDA), the Omega-K Algorithm (WKA) and the Time-domain Back-Projection (TDBP) were considered too.

Base on this simulator, the heavy computation loading was profiled and figured out by a flowchart. The back scattering computation block takes a high complexity with order of cubic. The NVIDIA CUDA technology was adopted, a high speedup rate was represented in more than 200 times in the experimental dihedral results. It means that this simulator is very suitable for very complex target where number of polygon is more than one million, and for larger target that the dimension is larger than 250 by 250 meters. The computing elapsed time is almost a half hour per SAR image clip. This calculation speed makes this simulator be feasible. Finally, an MSTAR (Moving and Stationary Target Acquisition and Recognition) vehicle and larger Buckingham palace complex target models were applied to the simulator in the final chapter. The verification of target’s dimension with feature can be found those results.關鍵字(中)★ 合成孔徑雷達

★ 模擬器

★ 目標資料庫

★ 回波訊號關鍵字(英)★ Synthetic aperture radar

★ Simulator

★ SAR database

★ SAR echo signal論文目次List of Figures viii

List of Tables x

Abbreviation Table xii

Chapter 1 Introduction 1

1.1 Motivation and Objectives 1

1.2 Organization of the dissertation 4

Chapter 2 Literature Review 5

2.1 The purpose of Raw data simulation 5

2.2 SAR simulation by GPU 6

2.3 What’s new in this dissertation 7

Chapter 3 Methodology 9

3.1 Surface / Edge Current Calculation 9

3.1.1 Incident Plane Definition 9

3.1.2 Reflection and PO Approximation 12

3.1.3 Surface Current Calculation 16

3.1.4 PTD Wedge Diffraction Approximation 19

3.2 Analytical SAR Echo Signal Model 22

3.2.1 Mathematical Analysis of SAR Echo Signal 22

3.2.2 Range Frequency Sampling 24

3.3 SAR Geometry 27

3.4 SAR Focusing Algorithm 33

3.4.1 Range-Doppler Algorithm 34

3.4.2 Omega-K Algorithm 37

3.4.3 Time-domain Back-projection 42

3.5 Flight Path Trajectory 51

3.5.1 Definition of Motion 51

3.5.2 Path Trajectory Simulation 54

3.5.3 Application for Motion Compensation 58

Chapter 4 Parallelization 61

4.1 Concept 62

4.2 Restriction 66

4.3 Implement 69

4.3.1 Step 0 - Fist parallel implement 69

4.3.2 Step 1 - Simple 73

4.3.3 Step 2 - Split a large kernel to small kernels 76

4.3.4 Step 3 - Move frequency independence section to outside of loop 78

4.3.5 Step 4 - Reduce device memory using patch 80

4.3.6 Step 5 - Register usage reduction 83

4.3.7 Step 6 - Core kernel function optimization more 84

4.3.8 Step 7 - Minor optimization 85

4.4 Speedup Restriction Factor 86

Chapter 5 Experimental Results 88

5.1 RCS Results 88

5.2 Combination Results of RCS and SAR 90

5.3 Dataset Simulation Results 92

5.3.1 MSTAR 92

5.3.2 Buckingham palace 99

Chapter 6 Conclusion 104

Chapter 7 Future work 106

Bibliography 107參考文獻[1] C.-Y. Chiang, K.-S. Chen, C.-T. Wnag, and T. Lee, “Fast Satellite SAR Image Simulation using GPU-based Algorithm,” Proc. of 2010 Remote Sensing Symposium Across Taiwan Strait, 15-19 March 2010, Chungli, Taiwan, pp. 1–4, Mar. 2010.

[2] Y.-L. Chang, C.-Y. Chiang, and K.-S. Chen, “SAR image simulation with application to target recognition,” Progress In Electromagnetics Research - PIER, vol. 119, pp. 35–57, 2011.

[3] U. Soergel, Radar Remote Sensing of Urban Areas (Remote Sensing and Digital Image Processing), 1st ed. Springer, 2010.

[4] Yongwei Sheng and D. E. Alsdorf, “Automated georeferencing and orthorectification of Amazon basin-wide SAR mosaics using SRTM DEM data,” IEEE Trans. Geosci. Remote Sensing, vol. 43, no. 8, pp. 1929–1940, Jul. 2005.

[5] T. Balz and U. Stilla, “Hybrid GPU-Based Single- and Double-Bounce SAR Simulation,” IEEE Trans. Geosci. Remote Sensing, vol. 47, no. 10, pp. 3519–3529, Jan. 2009.

[6] H.-J. Mametsa, F. Rouas, A. Berges, and J. Latger, “Imaging Radar Simulation in Realistic Environment Using Shooting and Bouncing Rays Technique,” SAR Image Analysis, Modeling, and Techniques IV, vol. 4543, pp. 34–41, 2002.

[7] J. C. Holtzman, V. S. Frost, J. L. Abbott, and V. H. Kaupp, “Radar Image Simulation,” IEEE Transactions on Geoscience Electronics, vol. 16, no. 4, pp. 296–303, 1978.

[8] G. Franceschetti, G. SARAS a synthetic aperture radar SAR raw signal simulator Franceschetti, M. Migliaccio, M. Migliaccio, D. Riccio, G. Schirinzi, and G. G. A. R. S. I. T. O. Schirinzi, “SARAS - a synthetic aperture radar (SAR) raw signal simulator.,” IEEE Trans. Geoscience and Remote Sensing, vol. 30, no. 1, pp. 110–123, 1992.

[9] J. M. Rius, M. Ferrando, and L. Jofre, “GRECO: graphical electromagnetic computing for RCS prediction in real time,” Antennas and Propagation Magazine, IEEE, vol. 35, no. 2, pp. 7–17, 1993.

[10] H. Hammer, T. Balz, E. Cadario, U. Soergel, U. ThOnnessen, and U. Stilla, “Comparison of SAR simulation concepts for the analysis of high resolution SAR data,” presented at the Synthetic Aperture Radar EUSAR, th European Conference on SAR, 2008, pp. 1–4.

[11] C. Kee and C. F. Wang, “Efficient Implementation of High-Frequency SBR-PO Method on GPU,” IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, 2013.

[12] Yubo Tao, Hai Lin, and Hujun Bao, “GPU-Based Shooting and Bouncing Ray Method for Fast RCS Prediction,” IEEE Trans. Antennas Propagat., vol. 58, no. 2, pp. 494–502, Aug. 2010.

[13] C. Y. Kee, C.-F. Wang, and T. T. Chia, “Optimizing high-frequency PO-SBR on GPU for multiple frequencies,” presented at the 2015 IEEE 4th Asia-Pacific Conference on Antennas and Propagation (APCAP), 2015, pp. 132–133.

[14] F. Zhang, C. Hu, W. Li, W. Hu, and H. C. Li, “Accelerating time-domain SAR raw data simulation for large areas using multi-GPUs,” Selected Topics in Applied …, 2014.

[15] L. Yu, X. Xie, and L. Xiao, “GPU-accelerated circular SAR echo data simulation of large scenes,” presented at the 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS), 2014, pp. 1–4.

[16] Kaizhi Wang, Hui Sheng, Xingzhao Liu, and Ming Zhou, “SAR echo simulation from numerous scattering cells based on GPU,” presented at the IET International Radar Conference 2013, 2013, pp. 0106–0106.

[17] T. Liu, K. Wang, and X. Liu, “SAR simulation for large scenes by ray tracing technique based on GPU,” presented at the IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium, 2013, pp. 1131–1134.

[18] H. Sheng, K. Wang, X. Liu, and J. Li, “A fast raw data simulator for the stripmap SAR based on CUDA via GPU,” 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, pp. 915–918, Jul. 2013.

[19] Hongbing Chen, Yulin Huang, and Jianyu Yang, “Airborne bistatic SAR echo simulator based on Multi-GPU platform,” presented at the 2011 IEEE CIE International Conference on Radar (Radar), 2011, pp. 76–78.

[20] W. Bingnan, Z. Fan, and X. Maosheng, “SAR raw signal simulation based on GPU parallel computation,” presented at the 2009 IEEE International Geoscience and Remote Sensing Symposium, 2009, pp. IV–617–IV–620.

[21] C. Zhu, Z. Xiang, K. Wang, and X. Liu, “A two-level simulator for spaceborne SAR,” presented at the 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar (APSAR), 2009, pp. 369–372.

[22] Y. Lu, K. Wang, X. Liu, and W. Yu, “A GPU based real-time SAR simulation for complex scenes,” presented at the International Radar Conference Surveillance for a Safer World RADAR, 2009, pp. 1–4.

[23] T. Balz, “Real-Time SAR Simulation on Graphics Processing Units,” presented at the EUSAR - th European Conference on Synthetic Aperture Radar, 2006, pp. 1–6.

[24] R. Zhang, J. Hong, and F. Ming, “SAR Echo and Image Simulation of Complex Targets Based on Electromagnetic Scattering,” Journal of Electronics & Information Technology, vol. 32, no. 12, pp. 2836–2841, Dec. 2010.

[25] F. Chatzigeorgiadis, “Development of Code for a Physical Optics Radar Cross Section Prediction and Analysis Application,” NAVAL POSTGRADUATE SCHOOL THESIS, 2004.

[26] H. Ling, R. C. Chou, and S. W. Lee, “Shooting and bouncing rays: Calculating the RCS of an arbitrarily shaped cavity,” IEEE Trans. Antennas Propagat., vol. 37, no. 2, pp. 194–205, 1989.

[27] F. Weinmann, “Ray tracing with PO/PTD for RCS modeling of large complex objects,” IEEE Trans. Antennas Propagat., vol. 54, no. 6, pp. 1797–1806, 2006.

[28] F. Weinmann, “UTD Shooting-and-Bouncing Extension to a PO/PTD Ray Tracing Algorithm,” Applied Computational Electromagnetics Society Journal, vol. 24, no. 3, pp. 281–293, Jun. 2009.

[29] K. M. Mitzner and N. C. H. C. A. DIV, Incremental Length Diffraction Coefficients. Ar Force Avionics Laberatory, Air Force System Command Wright-Patterson Air Force Ease, Chio, 1974.

[30] A. Michaeli, “Equivalent edge currents for arbitrary aspects of observation,” IEEE Trans. Antennas Propagat., vol. 32, no. 3, pp. 252–258, 1984.

[31] E. Knott, “The relationship between Mitzner‘s ILDC and Michaeli’s equivalent currents,” IEEE Trans. Antennas Propagat., vol. 33, no. 1, pp. 112–114, 1985.

[32] A. Michaeli, “Elimination of infinities in equivalent edge currents, part I: Fringe current components,” IEEE Trans. Antennas Propagat., vol. 34, no. 7, pp. 912–918, 1986.

[33] I. G. Cumming and F. H.-C. Wong, Digital processing of synthetic aperture radar data. Artech House Publishers, 2005.

[34] S. Madsen, “Estimating the Doppler centroid of SAR data,” Aerospace and Electronic Systems, IEEE Transactions on, vol. 25, no. 2, pp. 134–140, 1989.

[35] R. Bamler and H. Runge, “PRF-ambiguity resolving by wavelength diversity,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 29, no. 6, pp. 997–1003, 1991.

[36] F. Wong and I. Cumming, “A combined SAR Doppler centroid estimation scheme based upon signal phase,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 34, no. 3, pp. 696–707, 1996.

[37] C. Cafforio, C. Prati, and F. Rocca, “SAR data focusing using seismic migration techniques,” IEEE Transactions on Aerospace and Electronic Systems (ISSN 0018-9251), vol. 27, no. 2, pp. 194–207, Mar. 1991.

[38] A. Li, “Algoritthms For The Implementation Of STOLT Interpolation In SAR Processing,” Geoscience and Remote Sensing Symposium, 1992. IGARSS ′92. International, pp. 360–362, 1992.

[39] I. Cumming, Y. Neo, and F. Wong, “Interpretations of the omega-K algorithm and comparisons with other algorithms,” Geoscience and Remote Sensing Symposium, 2003. IGARSS ′03. Proceedings. 2003 IEEE International, vol. 3, pp. 1455–1458, 2003.

[40] M. A. Tolman, A detailed look at the Omega-k algorithm for processing synthetic aperture radar data. 2008.

[41] Z.-Y. Zhang and Z. Rong, “RMA imaging algorithm based on NUFFT′s,” Computer Engineering and Applications, vol. 17, pp. 33–34, 55, 2007.

[42] R. Stolt, “Migration by Fourier transform,” Geophysics, vol. 43, p. 23, 1978.

[43] Yuanxun Wang and Hao Ling, “A frequency-aspect extrapolation algorithm for ISAR image simulation based on two-dimensional ESPRIT,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 38, no. 4, pp. 1743–1748, 2000.

[44] C. Ozdemir, Inverse Synthetic Aperture Radar Imaging With MATLAB Algorithms. Hoboken, NJ, USA: John Wiley & Sons, 2012.

[45] M. I. Duersch, “Backprojection for synthetic aperture radar,” Department of Electrical and Computer Engineering, Brigham Young University, 2013.

[46] E. C. Zaugg, “Generalized Image Formation for Pulsed and LFM-CW Synthetic Aperture Radar,” Department of Electrical and Computer Engineering, Brigham Young University, 2010.

[47] D. G. Long and C. Stringham, “The Sample BYU CASIE-09 MicroASAR Dataset,” Center for Remote Sensing Brigham Young University, Oct. 2011.

[48] M. Zhang, C. Liu, and Y.-F. Wang, “Motion Compensation for Airborne SAR with Synthetic Bandwidth,” Journal of Electronics & Information Technology, vol. 33, no. 9, pp. 2114–2119, Sep. 2011.

[49] E. Zaugg, D. Long, and M. Wilson, “Improved SAR Motion Compensation without Interpolation,” 7th European Conference on Synthetic Aperture Radar , 02.-05. June 2008, Graf-Zeppelin-Haus, Friedrichshafen, Germany, EUSAR 2008, 2008.

[50] C. Fuxiang, B. Zheng, and Y. Jianping, “Motion compensation for airborne SAR,” presented at the Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on, 2000, vol. 3, pp. 1864–1867.

[51] Y. Chia-Feng, “Motion Compensation in Airborne Synthetic Aperture Radar Signal,” Institute of Mechanical Engineering College of Engineering National Chiao Tung University, 2004.

[52] K. A. C. deMacedo and R. Scheiber, “Precise Topography- and Aperture-Dependent Motion Compensation for Airborne SAR,” IEEE Geoscience and Remote Sensing Letters, vol. 2, no. 2, pp. 172–176, Apr. 2005.

[53] E. Alivizatos, A. Potsis, and N. Uzunoglu, “SAR Processing with motion compensation using the extended wavenumber algorithm,” Proceedings of EUSAR′04, VDE Verlag, Ulm, May, pp. 997–1000, 2004.

[54] J. Kirk, Signal based motion compensation for synthetic aperture radar. Technology Service Corporation, Los Angeles, CA (US), 1999.

[55] S. Auer, S. Hinz, and R. Bamler, “Ray-Tracing Simulation Techniques for Understanding High-Resolution SAR Images,” IEEE Trans. Geosci. Remote Sensing, vol. 48, no. 3, pp. 1445–1456, Mar. 2010.

[56] A. Boag, “A Fast Physical Optics (FPO) Algorithm for High Frequency Scattering,” IEEE Trans. Antennas Propagat., vol. 52, no. 1, pp. 197–204, Jan. 2004.

[57] S. Sefi, “Ray Tracing Tools for High Frequency Electromagnetics Simulations,” Universitetsservice US AB, Stockholm THESIS, 2003.

[58] T. Möller and B. Trumbore, “Fast, minimum storage ray-triangle intersection,” Journal of graphics tools, vol. 2, no. 1, pp. 21–28, 1997.

[59] C.-Y. Chiang and K.-S. Chen, “Simulation of complex target RCS with application to SAR image recognition,” 2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), 26-30 September 2011, Seoul, Korea, pp. 1–4, 2011.

[60] G. Margarit, J. J. Mallorqui, J. M. Rius, and J. Sanz-Marcos, “On the Usage of GRECOSAR, an Orbital Polarimetric SAR Simulator of Complex Targets, to Vessel Classification Studies,” IEEE Trans. Geosci. Remote Sensing, vol. 44, no. 12, pp. 3517–3526, Jan. 2006.

[61] K.-S. Chen, Principles of Synthetic Aperture Radar Imaging: A System Simulation Approach. CRC Press, 2016.指導教授任玄 范國清(Hsuan Ren Kuo-Chin Fan) 審核日期2019-1-24 推文facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu 網路書籤Google bookmarks del.icio.us hemidemi myshare