衛載合成孔徑雷達模擬影像負載平衡平行 處理運算研究

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：5

、訪客IP：18.191.33.207

姓名

孫傳禮(Chuan-Li Sun) 查詢紙本館藏

畢業系所

太空科學研究所

論文名稱

衛載合成孔徑雷達模擬影像負載平衡平行處理運算研究

相關論文

★ 運用模糊幾何理論對於Es層做自動分類及分析	★ GPS/MET及中壢DPS電離層遮蔽觀測比較
★ 結合NNSS與GPS/MET衛星資料於電離層斷層掃描觀測及其比較	★ 電離圖判讀與流星研究
★ 中壢動態式電離層觀測儀(dynasonde)訊號處理	★ 利用動態式電離層觀測儀觀測不規則體小尺度變化
★ GPS/MET遮蔽觀測與IRI模式foF2和hmF2之比較	★ GPS信號遮蔽觀測於電離層斷層掃描之模擬研究
★ ITS30-LITN觀測電離層不規則體閃爍現象	★ 中壢動態式電離層探測儀系統控制卡(CRAM Card)重建及測試
★ 運用ITS系統對於低緯度電離層斷層掃瞄的模擬與研究	★ GPS/MET遮蔽觀測foF2 numerical mapping與IRI 模式之比較分析
★ 低緯度電離層不規則體之結構研究	★ 利用福衛三號掩星觀測資料研究電離層增層現象
★ 運用臺灣自主電離層數值模式研究電離層赤道異常現象	★ 臺灣第二代動態式電離層探測儀之建置與資料處理

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]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

合成孔徑雷達（SAR）具備全天候偵照自然環境的條件，衛星 SAR
回波信號模擬架構可模擬雷達目標物回波訊號，並結合成像處理器來
建立偵照目標物影像資料庫，有效提升 SAR 影像偵照目標物辨識的準
確性。基於合成孔徑雷達影像目標物模擬系統需要耗費較長時間進行
全方位目標物回波信號運算，這裡我們建立負載平衡模式(LBM)的平
行處理架構來降低運算所需時間，此架構藉由高速網路結合資訊傳遞
介面(MPI)及多個圖形處理器(GPU)高速運算卡進行分散運算。
LBM 架構分為 MPI 管理機制及 GPU 管理機制兩種層次步驟，MPI
管理機制主要是結合靜態與動態負載平衡的優點，分成「先期評估階
段」、「工作管理階段」及「調整策略階段」等部分。GPU 管理機制
主要是運用 CUDA 程式呼叫 kernel 函數後，交由 GPU 執行，以便運用
大量 GPU 內的執行緒進行平行處理運算工作。經模擬 TerraSAR-X 和
RADARSAT-2 衛星 SAR 回波信號，並針對麥道 MD-80 飛機目標三維 CAD
圖像雷達散射截面（RCS）進行運算，在入射角固定 35 度、水平角度
是-180 度至 180 度，polygon 26500 時，實驗證明運用 LBM 的平行處
理架構演算法，完成雷達目標物截面積運算的時間大約提升為 4 核心
CPU 電腦的 40 倍。

摘要(英)

Synthetic Aperture Radar (SAR) is a powerful tool for studying the natural environment under all weather conditions during the whole day.
SAR system design and data-processing algorithm simulation is noted for its controllable parameters. The satellite SAR echo signal simulation framework can incorporate simulated radar target echo signals combined with a graphic processor to create a specific target image database,effectively raising SAR image specific target recognition accuracy.
In order to solve the satellite SAR echo signal simulation system taking too long in a full blown SAR image simulation for the raw target echo generation, we developed a “Load-Balancing Model (LBM)” algorithm that uses a Message Passing Interface and Graphics Processing Unit (MPI-GPU) with a parallel processing platform to provide an alternative choice for high performance parallel computing.
LBM is divided into two command mechanisms: MPI management
and GPU management. MPI management is mainly a combination of static and dynamic loads, with three balanced parts, including a pre-evaluation stage, management-work stage and adjust-strategy stage.
GPU management includes feedback and calculation stages. The CUDA program is used to call kernel functions executed by the GPU to perform parallel processing and computing tasks with a large amount of data.
The LBM algorithm is used to separate the intensive computing and control number of tasks, exploiting the contemporary GPU computation capability to reduce the inner loop load and improve the computing performance.
ix Both TerraSAR-X and RADARSAT-2 satellite SAR echo signals for McDonnell Douglas MD-80 aircraft targets with a spatial resolution of 3m in strip map were simulated and used to evaluate LBM performance.
The satellite SAR echo signal simulation system exports a
three-dimensional CAD model of the target of interest. The CAD model contains numerous grids or polygons, each associated with computed RCS as functions of incident and aspect angles for a given set of radar parameters.
We conducted a relevant experiment on a target radar cross
section (RCS) and improved its performance by a factor greater than 40, compared with a 4-core CPU used accelerated program.

關鍵字(中)

★ 合成孔徑雷達
★ 負載平衡平行處理
★ 訊息傳遞平行標準
★ 圖形處理晶片

關鍵字(英)

★ SAR
★ Load Balancing Model
★ MPI
★ GPU

論文目次

中文摘要 ………………………………………………………………… vii
英文提要 ………………………………………………………………… viii
致謝 ………………………………………………………………… x
圖目錄 ………………………………………………………………… xi
表目錄 ………………………………………………………………… xiii
符號說明 ………………………………………………………………… xiv
一、緒論…………………………………………………………… 1
1-1 研究背景……………………………………………………… 1
1-2 研究目標……………………………………………………… 4
1-3 章節簡介……………………………………………………… 7
二、衛載SAR影像模擬流程……………………………………… 8
2-1 簡介…………………………………………………………… 8
2-2 模擬流程介紹………………………………………………… 11
2-3 模擬公式說明………………………………………………… 13
2-3-1 幾何公式說明………………………………………………… 13
2-3-2 成像模擬流程與方法………………………………………… 17
2-3-3 影像模擬結果與驗證………………………………………… 21
2-3-4 模擬時間說明………………………………………………… 29
三、叢集式電腦運算架構介紹…………………………………… 32
3-1 簡介…………………………………………………………… 32
3-2 叢集式電腦架構……………………………………………… 32
3-2-1 MPI介紹……………………………………………………… 34
3-2-2 GPU介紹……………………………………………………… 42
3-2-3 CUDA介紹……………………………………………………… 43
3-2-4 GPU卡規格介紹……………………………………………… 52
3-3 MPI與GPU傳遞機制………………………………………… 54
四、負載平衡機制………………………………………………… 56
4-1 簡介…………………………………………………………… 56
4-2 負載平衡機制介紹…………………………………………… 58
4-2-1 LBM機制概觀………………………………………………… 59
4-2-2 MPI管理機制說明…………………………………………… 60
4-2-3 GPU管理機制說明…………………………………………… 66
4-3 MPI管理機制………………………………………………… 67
五、實驗結果……………………………………………………… 69
5-1 程式運作介紹………………………………………………… 69
5-2 負載平衡觸發條件…………………………………………… 71
5-3 實驗環境及數據……………………………………………… 75
5-3-1 單節點多GPU工作環境……………………………………… 75
5-3-2 多節點單GPU工作環境……………………………………… 78
5-2 雷達影像模擬結果…………………………………………… 80
六、結論…………………………………………………………… 86
參考文獻 ………………………………………………………………… 88

參考文獻

﹝1﹞Oliver, C., and S. Quegan, 2004: Understanding synthetic aperture radar images, SciTech Publishing. Inc.
﹝2﹞Guindon, B. 1993: Development of a SAR Data Acquisition Planning Tool (SARPIAN) Based on Image Simulation, International Journal of Remote Sensing, 14, 2, 333-344.
﹝3﹞Camporeale, C., and G. Galati, 1991: Digital Computer Simulation of Synthetic Aperture Systems and Images, European Transactions on Telecommunications, 2, 3, 343-352.
﹝4﹞2013:電磁學基礎(1)關於電磁場的一些疑問, 程式人雜誌10月號, http://programmermagazine.github.io/201310/htm/science1.html
﹝5﹞Curlander, C., and N. McDonough, 1991: Synthetic Aperture Radar：Systems and Signal Processing, John Wiley & Sons, Inc.
﹝6﹞National Secience and Technology Center for Disaster Reduction,災害情資網, http://eocdss.ncdr.nat.gov.tw/ncdrwebv2/
﹝7﹞Chen. K. S., C. Y. Chu., and C. T. Wang., Nation Central University Research Center for Taiwan Economic Development, http://www.narl.org.tw/ext/po/po_attach.php?po_file_id=16871
﹝8﹞Rihaczek, A. W., and S. J. Hershkowitz, 2000: Theory and Practice of Radar Target Identification, Artech House.
﹝9﹞Huang, C. W., and K. C. Lee, 2010: Frequency-diversity RCS based target recognition with ICA projection, Journal of Electromagnetic Waves and Applications, 24, 17-18, 2547-2559.
﹝10﹞Guo, K. Y., Q. Li, and X. Q. Sheng, 2010: A precise recognition method of missile warhead and decoy in multi-target scene, Journal of Electromagnetic Waves and Applications, 24, 5-6, 641-652.
﹝11﹞Tian, B., D. Y. Zhu, and Z. D. Zhu, 2011: A novel moving target detection approach for dual-channel SAR system, Progress In Electromagnetics Research, 115, 191-206.
﹝12﹞Wang, X. F., J. F. Chen, Z. G. Shi, and K. S. Chen, 2011: Fuzzy-control-based particle filter for maneuvering target tracking, Progress In Electromagnetics Research, 118, 1-5.
﹝13﹞Lee, J. S. and E. Pottier, 2009: Polarimetric Radar Imaging: From Basics to Applications, CRC Press.
﹝14﹞Margarit, G., J. J. Mallorqui, J. M. Rius, and J. Sanz-Marcos, 2006: On the usage of GRECOSAR, an orbital polarimetric SAR simulator of complex targets, to vessel classification studies, IEEE Trans. Geoscience and Remote Sens., 44, 3517-3526.
﹝15﹞Lee, J. S., 1980: Digital image enhancement and noise filtering by use of local statistics, IEEE Trans. Pattern Anal. Mach. Intell., 2, 165-168.
﹝16﹞Kasim , H., V. March1, R. Zhang, and S. See. 2008: Survey on Parallel Programming Mode, Proceedings of the IFIP International Conference on Network and Parallel Computing.
﹝17﹞Soumekh, M., 1997: Moving target detection in foliage using along track monopulse synthetic aperture radar imaging,IEEE Transactions on Image Processing, Aug, 6, 8, 1148–1163.
﹝18﹞Koskinen, J.T., J.T. Pulliainen, and M.T. Hallikainen, 1997: The use of ERS-1 SAR data in snow melt monitoring, IEEE Transactions on Geoscience and Remote Sensing, May, 35, 3, 601-610.
﹝19﹞Sharma, R. K., B.S. Kumar, N. M. Desai, and V.R. Gujraty, 2008: SAR for disaster management, IEEE Aerospace and Electronic Systems Magazine, June, 23, 6, 4-9.
﹝20﹞Keydel, E. R., S. W. Lee, and J. T. Moore, 1996: MSTAR Extended
Operating Conditions, Proc of SPIE, 2757, 228-242.
﹝21﹞Ross, T., S. Worrell, V. Velten, et al. 1998: Standard SAR ATR Evaluation Experiment Using the MSTAR Public Release Data Set, Proc of SPIE, 3370, 566- 573.
﹝22﹞Potter, L. C. and R. L. Moses, 1997: Attributed Scattering Centers for SAR ATR, IEEE Trans. on Image Process, 6, 1, 79-91.
﹝23﹞Novak, L.M., G.J. Owirka, and A.I.Weaver, 1999: Automatic target recognition using enhanced resolution SAR data,IEEE Trans. on AES., 35, 1, 157-175.
﹝24﹞Ross, T.D. et al., 1999: SAR ATR: so what’s the problem? An MSTAR perspective, Proc of SPIE, 3721, 662-672.
﹝25﹞DeVORE, M.D. and J.A.O. Sullivan, 2002: Performance Complexity Study of Several Approaches to Automatic Target Recognition from SAR Images, IEEE Trans. on AES., 38, 2, 632-648.
﹝26﹞Cetin, M. et al, 2003: Feature Enhancement and ATR Performance Using Nonquadratic Optimization-Based SAR Imaging, IEEE Trans. on AES., 39, 4, 1375-1395.
﹝27﹞Pettersson, M.I. 2004: Detection of Moving Targets in Wideband SAR, IEEE Trans. on AES., 40, 3, 780-796.
﹝28﹞Bordoni, F. et al., 2005: Multibaseline Cross-Track SAR Interferometry using Interpolated Arrays, IEEE Trans. on AES., 41, 4, 1472-1481.
﹝29﹞Oliver C., and S. Quegan, 1998: Understanding Synthetic Aperture RadarImages, Boston Artech House.
﹝30﹞Cafforio, C., C. Prati, and F. Rocca. 1987: SAR real-time on board processing: The polyphase algorithm, in Proc. Int. Conf. on Supercomputing, 248-253.
﹝31﹞Perry, R. P., and L. W. Martinson, 1978: Radar matched filtering, in Radar Technology. Nonvood, MA: Artech House, 11, 163-169.
﹝32﹞Sack, M. et al.,1985: Application of efficient Linear FM matched filtering algorithms to synthetic aperture radar processing, IEE Proc., 132, 1, 45-57.
﹝33﹞Vant, M. R., and K. H. Wu, 1984: A digital SAR processor based on the coherent subaperture addition technique, in Proc. Inf. Radar Conf, May, 425-429.
﹝34﹞Wu, K. H., and M. R. Vant, 1985: Extensions to the step transform SAR processing technique, IEEE Trans. Aerosp. Electron. Syst., May, AES-21, 3, 338-344.
﹝35﹞Wu, K. H., and M. R. Vant, 1984: Coherent subaperture processing techniques for SAR, Canadian Communications Research Center, Rep., Jan, 1388.
﹝36﹞Bolognani, S. R. Oboe, and M. Zigliotto, 1999: Sensorless Full-Digital PMSM Drive With EKF Estimation of Speed and Rotor Position , IEEE Transactions onIndustrial Electronics, 46(1), 184-191.
﹝37﹞Bolognani, S., L. Tubiana, and M. Zigliotto. 2003: Extended Kalman Filter Tuning in Sensorless PMSM Drives. IEEE Transactions on Industry Applications, 39(6), 1741-1747.
﹝38﹞Zhang, S. S., and J. Chen, 2008: A echo simulation algorithm for natural scene, IEEE Radar Conference.
﹝39﹞Balz, T., and U. Stilla, 2009:Hybrid GPU-based single and double bounce SAR simulation, IEEE Transactions on Geoscience and Remote Sensing, 47(10), 3519-3529.
﹝40﹞Cao, N., and H. Lu, 2009: Efficient SAR raw data simulation based on parallel computation in hybrid domain, WRI World Congress on Computer Science and Information Engineering.
﹝41﹞Cumming, G., and H. Wong, 2004: Digital Processing of Synthic Aperture Radar Data: Algorithms and Implementation, Boston, MA:Artech House.
﹝42﹞Pike, T., 1986: Analysis of ERS-1 SAR Performance through Simulation, Proceedings of IEEE National Radar Conference, IEEE, 13-18.
﹝43﹞Jung, C., J. Jung, T. Oh, and Y. Kwag, 2008: SAR Image Quality Assessment in Real Clutter Environment, Synthetic Aperture Radar (EUSAR), 7th European Conference on.
﹝44﹞Lu, X., and H. Sun, 2007: Parameter assessment for SAR image quality evaluation system, in Proc. 1st Asian and Pacific Conf. Synthetic Aperture Radar APSAR 58–60.
﹝45﹞Eineder, M. et al., 2008: TerraSAR-X Ground Segment, Basic Product Specification Document, Cluster Applied Remote Sensing (CAF) Oberpfaffenhofen(Germany).
﹝46﹞張西亞、王順泰、周朝宜、陳德民、吳長興、李金泓、謝志偉, 2007:高效能電腦叢集的發展與趨勢, 物理雙月刊，第五期第二十九卷.
﹝47﹞2004: 中央氣象局超高速運算電腦系統的應用, 政府機關資訊通報, http://readopac2.ncl.edu.tw/nclJournal/search/detail.jsp?dtdId=000040&search_type=detail&la=ch&checked=&unchecked=&sysId=0004087767
﹝48﹞Irwin, B., and A. N. 2009: GPU packet classification using OpenCL: a consideration of viable classification methods, SAICSIT Conf.ACM, 160-169.
﹝49﹞Charalambous, P. T. M., and A. Stamatakis, 2005: Initial experiences porting a bioinformatics application to a graphics processor, Advances in Informatics, 415-425.
﹝50﹞鄭守成, 2002: C語言 MPI平行計算程式設計.
﹝51﹞Henty, D. S., 2000: Performance of Hybrid Message-Passing and Shared-Memory Parallelism for Discrete Element Modeling, Proc. of the Supercomputing (SC).
﹝52﹞Noaje, G., M. Krajecki, and C. Jaillet, 2010: MultiGPU computing using MPI or OpenMP, in Proceedings of the 2010 IEEE 6th International Conference on Intelligent Computer Communication and Processing (ICPP).
﹝53﹞Loper, J., and S. Parr, 2007: Energy efficiency in data centers: A new policy frontier, Environ. Qual. Manage, 16, 83-97.
﹝54﹞Setoain, J., C.Tenllado, and M.Prieto, et al. 2006: Parallel Hyperspectral Image Processing on Commodity Graphics Hardware. Proceedings of the International Conference on Parallel Processing Workshops, 465-472.
﹝55﹞Setoain, J., M. Prieto, C. Tenllado, A. Plaza, and F. Tirado, 2007: Parallel Morphological Endmember Extraction Using Commodity Graphics Hardware, IEEE Geoscience and Remote Sensing Letters, 441-445.
﹝56﹞Balz, T., and N. Haala, 2006: Improved Real-Time SAR Simulation in Urban Areas, International Geoscience and Remote Sensing Symposium (IGARSS), 3631-3634.
﹝57﹞CUDA, http://en.wikipedia.org/wiki/CUDA
﹝58﹞NVIDIA Corporation, 2014: NVIDIA CUDA C PROGRAMMING GUIDE, http://docs.nvidia.com/cuda/pdf/CUDA_C_Programming_Guide.pdf, August.
﹝59﹞Heresy Space, nVidia CUDA簡介, http://kheresy.wordpress.com/2007/10/31/nvidia-cuda-簡介/
﹝60﹞Grauer-Gray, S. C. Kambhamettu, and K. Palaniappan. 2006: GPU Implementation of Belief Propagation Using CUDA for Cloud Tracking and Reconstruction, Pattern Recognition in Remote Sensing (PRRS 2008). 1-4.
﹝61﹞Aila, T., and S. Laine. 2009: Understanding the efficiency of ray traversal on GPUs, In Proceedings of High Performance Graphics, August, 145–149.
﹝62﹞Steven, G., J. Bigler, A. Dietrich, H. Friedrich, J. Hoberock, D. Luebke, D. McAllister, M. McGuire, K. Morley, A. Robison, and M. Stich. 2010: OptiX: A general purpose ray tracing engine. ACM Transactions on Graphics, August, 29, 3.
﹝63﹞Cederman, D., and P. Tsigas, 2011: Dynamic load balancing using work-stealing, In Wen-mei W. Hwu, editor, GPU Computing Gems, October, 2, 35, 485–499.
﹝64﹞Tzeng, S., A. Patney, and D. Owens, 2010: Task management for irregular-parallel workloads on the GPU, In Proceedings of High Performance Graphics, June, 29–37.
﹝65﹞Arora, S., D. Blumofe, and C. Plaxton, 1998: Thread scheduling for multiprogrammed multiprocessors, In Proceedings of the Tenth Annual ACM Symposium on Parallel Algorithms and Architectures, June/July, 119–129.
﹝66﹞Marc, H., and L. Willebeek, 1993: Strategies for dynamic load balancing on highly parallel computers, IEEE Transactions on Parallel and Distributed System.
﹝67﹞Chi-Chung, H., and S.T. Chanson, 1999: Hydrodynamic load balancing, IEEE Transactions on Parallel and Distributed System.
﹝68﹞Srisuresh, P., and D. Gan, 1998: Load Sharing using IP Network Address Translation(LSNAT), August, RFC 2391.
﹝69﹞Richard, B., L. Dreak, M. Oster, and L.Williamson,1999: Achieving Load Balance and Effective Caching in Clustered Web Servers, University of Saskatchewan, January 20.
﹝70﹞Padhy, R.P., and P.G. Prasad, 2011: Load Balancing in Cloud Computing System, Department of Computer Science and Engineering National Institute of Technology, 1-56.
﹝71﹞Colajanni, M., S. Yu, and M. Dias, 1998: Analysis of Task Assignment Policies in Scalable Distributed web-server systems, IEEE Tran. On Parallel and Distributed Systems, 9, 6, 585-600.
﹝72﹞Cardellini, V. E. Casalicchio, M. Colajanni, and S. Yu, 2002: The state of the art in locally distributed Web-server systems, ACM Computing Surveys, June, 34, 2, 263-311.
﹝73﹞Pai, S., M. Aron, Banga, M. Svendsen, P. Druschel, W. Zwaenepoel, and E. Nahum, 1998: Locality-Aware Request Distribution in Cluster-based Network Servers, Proc. ACM Arch. Support for Progr. Languages Conf., Octcber,535-544.
﹝74﹞Shi, R., S. Potluri, K. Hamidouche, Xi. Lu, Tomko, K.Ohio State Univ., Panda, D.K. 2013: A Scalable and Portable Approach to Accelerate Hybrid HPL on Heterogeneous CPU-GPU Clusters, in 2013 IEEE International Conference , Sept, 23-27, 1-8.
﹝75﹞Song, F. and J. Dongarra, 2012: A Scalable Framework for Heterogeneous GPU-based Clusters, in Proceedings of the 24th ACM symposium on Parallelism in algorithms and architectures (SPAA).
﹝76﹞Medhi, J., 1991: Stochastic Models in Queueing Theory, Academic Press.
﹝77﹞Bing, L., and S. Feng, 2007: Research on Dynamic Load Balancing Algorithm Based on Message Passing Mechanism, Computer Engineering, 5, 58.
﹝78﹞Shi, W., and Z. Tang, 1999: Dynamic Computation Scheduling for Load Balancing in Home-based Software DSMs, in Proceedings of the 1999 International Symposium on Parallel Architectures, Algorithms and Networks, IEEE Computer Press, Perth, Australia, June.
﹝79﹞Lei, W., H. Yongzhong,　C. Xin, et al. 2008: Task scheduling of parallel processing in CPU-GPU collaborative environment, Proc of International Conference on Computer Science and Information Technology. Piscataway, IEEE Press, 228-232.
﹝80﹞Guim, F., I. Rodero, J. Corbalan, et al, 2010: Enabling GPU and manycore systems in heterogeneous HPC environments using memory consideration , Proc of 12th IEEE International Conference on High Performance Computing and Communications. Los Alamitos, California: IEEE Computer Society Press, 146-155.
﹝81﹞Sun, C. L., L. C. Tsai, and C. Y. Chiang, 2016: SAR Image simulateion using the LBM algorithm on MPI-GPU, Innovative Applications of Radar and LiDAR Remote Sensing of Terrestrial, Atmospheric and Oceanic Sciences, Aug.

指導教授

蔡龍治

審核日期

2016-7-15

推文