一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機號碼
標(biāo)題
留言內(nèi)容
驗證碼

基于改進多重測量向量模型的SAR成像算法

陳一暢,  張群,  楊婷,  羅迎

陳一暢, 張群, 楊婷, 羅迎. 基于改進多重測量向量模型的SAR成像算法[J]. 電子與信息學(xué)報, 2016, 38(10): 2423-2429. doi: 10.11999/JEIT151391
引用本文: 陳一暢, 張群, 楊婷, 羅迎. 基于改進多重測量向量模型的SAR成像算法[J]. 電子與信息學(xué)報, 2016, 38(10): 2423-2429. doi: 10.11999/JEIT151391
CHEN Yichang, ZHANG Qun, YANG Ting, LUO Ying. A Novel SAR Imaging Algorithm Based on Modified Multiple Measurement Vectors Model[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2423-2429. doi: 10.11999/JEIT151391
Citation: CHEN Yichang, ZHANG Qun, YANG Ting, LUO Ying. A Novel SAR Imaging Algorithm Based on Modified Multiple Measurement Vectors Model[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2423-2429. doi: 10.11999/JEIT151391

基于改進多重測量向量模型的SAR成像算法

doi: 10.11999/JEIT151391
  • 1.

    (空軍工程大學(xué)信息與導(dǎo)航學(xué)院 西安 710077) ②(復(fù)旦大學(xué)電磁波信息科學(xué)教育部重點實驗室 上海 200433) ③(空軍預(yù)警學(xué)院黃陂士官學(xué)校 武漢 430000) ④(中國人民解放軍95133部隊 武漢 430000)

基金項目: 

國家自然科學(xué)基金(61471386)，中國博士后基金(2015M570815)，陜西省統(tǒng)籌創(chuàng)新工程-特色產(chǎn)業(yè)創(chuàng)新鏈項目(2015KTTSGY04-06)

A Novel SAR Imaging Algorithm Based on Modified Multiple Measurement Vectors Model

  • 1.

    (Institute of Information and Navigation, Air Force Engineering University, Xi&rsquo

Funds: 

The National Natural Science Foundation of China (61471386), The Postdoctoral Science Foundation of China (2015M570815), The Overall Innovation and Characteristic Industry Innovation Chain Project of Shaanxi Province (2015KTTSGY04-06)

  • 摘要: 近年來，基于壓縮感知(Compressed Sensing, CS)理論的稀疏場景SAR成像成為研究熱點。在CS理論中，對于具有相同稀疏結(jié)構(gòu)的聯(lián)合稀疏目標(biāo)信號源，多重測量向量(Multiple Measurement Vectors, MMV)模型可以獲得優(yōu)于單重測量矢量(Single Measurement Vector, SMV)模型的重構(gòu)性能。然而，在距離徙動(Range Migration)不可忽略的應(yīng)用場景，SAR各脈沖回波1維距離像具有不完全相同的稀疏結(jié)構(gòu)，這使得無法在SAR應(yīng)用中直接引入MMV模型。該文針對MMV模型與SAR距離徙動的矛盾，提出一種改進的MMV模型。在該模型下，各方位向位置對應(yīng)的1維距離像的稀疏結(jié)構(gòu)不要求完全相同，而是符合距離徙動特性。論文所提出的RM-OMP算法根據(jù)符合距離徙動特性的稀疏結(jié)構(gòu)搜索稀疏信號支撐集，可以準(zhǔn)確地重建稀疏信號源。論文采用仿真數(shù)據(jù)和實測數(shù)據(jù)實驗驗證了所提模型和算法的有效性。
    Abstract: Recently, the Compressed Sensing (CS) theory becomes the researching hot point in SAR imaging. The Multiple Measurement Vectors (MMV) model of CS theory can be used to effectively represent the jointly sparse signals, and it can obtain better performance than Single Measurement Vector (SMV) model. Because the SAR range profiles at different pulses have different sparse structures, which result in the MMV model can not be directly used in the scenario of synthetic aperture radar imaging. In this paper, a modified MMV model is proposed for SAR imaging, and the Range Migration (RM) effect is embedded into the proposed model. Correspondingly, a modified Orthogonal Matching Pursuit (OMP) algorithm is developed to obtain the high-resolution range profile. Experiments based on simulated and measured data demonstrate the validity of the proposed model and the algorithm.
    • HTML全文
  • JUN Shi, ZHANG Xiaoling, and YANG Jianyu. Principle and methods on bistatic SAR signal processing via time correlation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(10): 3163-3178. doi: 10.1109/TGRS. 2008.920369.
    SHEN Fangfang, ZHAO Guanghui, LIU Zicheng, et al. SAR imaging with structural sparse representation[J]. IEEE Journal of Selected Topocs in Applied Earth Observations and Remote Sensing, 2015, 8(8): 3902-3910. doi: 10.1109/JSTARS.2014.2364294.
    BAMLER R. A comparison of Range-Doppler and wavenumber domain SAR focusing algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(4): 706-713. doi: 10.1109/36.158864.
    DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/TIT.2006.871582.
    邱偉, 趙宏鐘, 陳建軍, 等. 基于平滑范數(shù)的高分辨雷達一維成像研究[J]. 電子與信息學(xué)報, 2011，33(12): 2869-2874. doi: 10.3724/SP.J.1146.2011.00418.
    QIU Wei, ZHAO Hongzhong, CHEN Jianjun, et al. Highresolution radar one-dimendional imaging based on smoothed norm[J]. Journal of Electronics Information Technology, 2011, 33(12): 2869-2874. doi: 10.3724/SP. J.1146.2011.00418.
    BU Hongxia, TAO Ran, BAI Xia, et al. A novel SAR imaging algorithm based on compressed sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5): 1003-1007. doi: 10.1106/LGRS.2014.2372319.
    TELLO A M, LOPEZ D P, and MALLORQUI J J. A novel strategy for radar imaging based on compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 4285-4295. doi: 10.1109/TGRS.2010.2051231.
    ONHON N O and CETIN M. A sparsity driven approach for joint SAR imaging and Phase error correction[J]. IEEE Transactions on Image Processing, 2011, 21(4): 2075-2088. doi: 10.1109/TIP.2011.2179056.
    XIA Linuo, ZHU Daiyin, and JIANG Rui. A research on SAR autofocusing algorithms for compressive sensing technique [C]. IET International Radar Conference, Xi'an, 2013: 1-6.
    GU Fu-fei, ZHANG Qun, CHI Long, et al. A novel motion compensating method for MIMO-SAR imaging based on compressed sensing[J]. IEEE Sensors Journal, 2015, 15(4): 2157-2165. doi: 10.1109/JSEN.2014.2371451.
    CETIN M, STOJANOVIC I, ONHON N O, et al. Sparsity-driven synthetic aperture radar imaging: reconstruction, autofocusing, moving targets, and compressed sensing[J]. IEEE Signal Processing Magazine, 2014, 31(4): 27-40. doi: 10.1109/MSP.2014.2312834.
    YANG Jiefang and ZHANG Yunhua. Novel compressive sensing-based Dechirp-Keystone algorithm for synthetic aperture radar imaging of moving target[J]. IET Radar, Sonar Navigation, 2015, 9(5): 509-518. doi: 10.1049/iet-rsn. 2014.0306.
    CHEN J and HUO X. Theoretical results on sparse representations of multiple-measurement vectors[J]. IEEE Transactions on Signal Processing, 2006, 54(12): 4634-4643. doi: 10.1109/TSP.2006.881263.
    YANG J, BOUZERDOUM A, TIVIVE F H C, et al. Multiple-measurement vector model and its application to through-the-wall radar imaging[C]. IEEE ICASS, Prague, Czech Republic, 2011: 2672-2675.
    陳一暢, 張群, 陳校平, 等. 多重測量矢量模型下的稀疏步進頻率SAR成像算法[J]. 電子與信息學(xué)報, 2014，36(12): 2986-2993. doi: 10.3724/SP.J.1146.2013.01831.
    CHEN Yichang, ZHANG Qun, CHEN Xiaoping, et al. An imaging algorithm of sparse stepped frequency SAR based on multiple measurement vectors model[J]. Journal of Electronics Information Technology, 2014, 36(12): 2986-2993. doi: 10.3724/SP.J.1146.2013.01831.
    TROPP J A, GILBERT A C, and STRAUSS M J. Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit[J]. Signal Processing, 2006, 86(3): 572-588. doi: 10.1016/j.sigpro.2005.05.030
    TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666. doi: 10.1109/TIT.2007.909108.
    CUMMING L G and WONG F H. Digital Processing of Synthetic Aperture Radar Data: Algorithm and Implementation[M]. MA: Artech House, 2005: 393-394.
  • 加載中
計量
  • 文章訪問數(shù):  1405
  • HTML全文瀏覽量:  130
  • PDF下載量:  531
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2015-12-09
  • 修回日期:  2016-05-03
  • 刊出日期:  2016-10-19

目錄

    /

    返回文章
    返回