稀疏線性調(diào)頻步進(jìn)信號(hào)ISAR成像觀測(cè)矩陣自適應(yīng)優(yōu)化方法
doi: 10.11999/JEIT170554
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1.
(空軍工程大學(xué)信息與導(dǎo)航學(xué)院 西安 710077)
國(guó)家自然科學(xué)基金(61631019, 61471386),陜西省青年科技新星計(jì)劃(2016KJXX-49)
Adaptive Measurement Matrix Optimization for ISAR Imaging with Sparse Frequency-stepped Chirp Signals
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1.
(Institute of Information and Navigation, Air Force Engineering University, Xi&rsquo
The National Natural Science Foundation of China (61631019, 61471386), The Youth Science and Technology New Star Program of Shaanxi Province (2016KJXX-49)
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摘要: 基于壓縮感知(CS)理論的稀疏線性調(diào)頻步進(jìn)信號(hào)(SFCS)逆合成孔徑雷達(dá)(ISAR)成像技術(shù)能夠從少量觀測(cè)數(shù)據(jù)中高概率重構(gòu)出目標(biāo)像,其中,觀測(cè)矩陣的優(yōu)化設(shè)計(jì)是提高成像質(zhì)量和減少觀測(cè)數(shù)據(jù)量的有效途徑。然而,現(xiàn)有的觀測(cè)矩陣優(yōu)化設(shè)計(jì)研究通常沒(méi)有考慮目標(biāo)特征信息的有效利用,對(duì)目標(biāo)的自適應(yīng)能力不足。因此,該文在充分利用目標(biāo)特征信息的基礎(chǔ)上,結(jié)合稀疏SFCS信號(hào)的實(shí)際物理觀測(cè)過(guò)程,提出一種ISAR成像觀測(cè)矩陣自適應(yīng)優(yōu)化方法。該方法首先建立參數(shù)化稀疏表征成像模型以解決稀疏SFCS信號(hào)多普勒敏感問(wèn)題,在此基礎(chǔ)上,以在達(dá)到成像質(zhì)量要求條件下使用最少觀測(cè)數(shù)據(jù)量獲得最優(yōu)成像結(jié)果為目標(biāo)對(duì)觀測(cè)矩陣進(jìn)行自適應(yīng)優(yōu)化設(shè)計(jì),最終能夠利用最少的數(shù)據(jù)量獲得滿意的目標(biāo)成像結(jié)果。仿真實(shí)驗(yàn)驗(yàn)證了該算法的有效性。
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關(guān)鍵詞:
- 觀測(cè)矩陣優(yōu)化 /
- 參數(shù)化稀疏表征 /
- 稀疏線性調(diào)頻步進(jìn)信號(hào)
Abstract: The ISAR imaging technology with sparse Stepped-Frequency Chirp Signals (SFCS) based on Compressive Sensing (CS) theory can construct the target image from a few of measurements with high probability, where the measurement matrix optimization is an effective way of improving the imaging quality and reducing the measurements. However, most of the existing measurement matrix optimization methods do not utilize the target characteristic, which leads to low adaptive ability of target. Therefore, an adaptive measurement matrix optimization method for Inverse Synthetic Aperture Radar (ISAR) Imaging with sparse SFCS is proposed in this paper, where the actual physical observation process is considered and the target characteristics are utilized to optimize the measurement matrix. In the method, a parametric sparse representation model of ISAR imaging is established to solve the Doppler sensitivity firstly. On the basis, the measurement matrix is optimized with the goal of obtaining the best target image with the minimum measurements under a given image quality requirement. As a result, the expected imaging results can be obtained with minimum measurements by using the optimized measurement matrix. The effectiveness of the proposed method is demonstrated by experiments. -
ZHU Feng, ZHANG Qun, LUO Ying, et al. A novel cognitive ISAR imaging method with random stepped frequency chirp signal[J]. Science China Information Science, 2012, 55(8): 1910-1924. doi: 10.1007/s11432-012-4629-0. 徐丹蕾, 杜蘭, 劉宏偉, 等. 基于復(fù)數(shù)因子分析模型的步進(jìn)頻數(shù)據(jù)壓縮感知[J]. 電子與信息學(xué)報(bào), 2015, 37(2): 315-321. doi: 10.11999/JEIT140407. XU Danlei, DU Lan, LIU Hongwei, et al. Compressive sensing using complex factor analysis for stepped-frequency data[J]. Journal of Electronics Information Technology, 2015, 37(2): 315-321. doi: 10.11999/JEIT140407. 呂明久, 李少東, 楊軍, 等. 基于隨機(jī)調(diào)頻步進(jìn)信號(hào)的高分辨率ISAR成像方法[J]. 電子與信息學(xué)報(bào), 2016, 38(12): 3129-3136. doi: 10.11999/JEIT160177. L Mingjiu, LI Shaodong, YANG Jun, et al. High resolution ISAR imaging method based on random chirp frequency- stepped signal[J]. Journal of Electronics Information Technology, 2016, 38(12): 3129-3136. doi: 10.11999/ JEIT160177. ELAD M. Optimized projections for compressed sensing[J]. IEEE Transactions on Signal Processing, 2007, 55(12): 5695-5702. doi: 10.1109/TSP.2007.900760. YAN Wenjie, WANG Qiang, and SHEN Yi. Shrinkage-based alternating projection algorithm for efficient measurement matrix construction in compressive sensing[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(5): 1073-1048. doi: 10.1109/tim.2014.2298271. 劉杰平, 楊朝煜, 方杰, 等. 改進(jìn)的基于梯度投影的Gram觀測(cè)矩陣優(yōu)化算法[J]. 華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版), 2016, 44(8): 62-65. doi: 10.13245/j.hust.160813. LIU Jieping, YANG Chaoyu, FANG Jie, et al. Improved optimization algorithm of the Gram measurement matrix based on gradient projection[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2016, 44(8): 62-65. doi: 10.13245/j.hust.160813. ABATZOGLOU T J and GHEEN G O. Range, radical velocity, and acceleration MLE using radar LFM pulse train[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 34(4): 1070-1083. doi: 10.1109/7.722676. 毛二可, 龍騰, 韓月秋. 頻率步進(jìn)雷達(dá)數(shù)字信號(hào)處理[J]. 航空學(xué)報(bào), 2001, 22(b06): 16-24. doi: 10.3321/j.issn:1000-6893. 2001.z1.003. MAO Erke, LONG Teng, and HAN Yueqiu. Digital signal processing of stepped frequency radar[J]. Acta Aeronautica et Astronautica Sinica, 2001, 22(b06): 16-24. doi: 10.3321/j.issn: 1000-6893.2001.z1.003. 呂明久, 李少東, 楊軍, 等. 基于全局最小熵的隨機(jī)稀疏調(diào)頻步進(jìn)信號(hào)運(yùn)動(dòng)補(bǔ)償方法[J]. 系統(tǒng)工程與電子技術(shù), 2016, 38(8): 1744-1751. doi: 10.3969/j.issn.1001-506X.2016.08.06. L Mingjiu, LI Shaodong, YANG Jun, et al. Motion- compensation method based on global minimum entropy for random sparse stepped-frequency chirp signal[J]. Systems Engineering and Electronics, 2016, 38(8): 1744-1751. doi: 10.3969/j.issn.1001-506X.2016.08.06. 王曉東. 基于步進(jìn)頻率的目標(biāo)成像與速度精確測(cè)量方法[J]. 四川兵工學(xué)報(bào), 2015, 36(5): 115-118. doi: 10.11809/ scbgxb2015.05.03. WANG Xiaodong. Target imaging and velocity measurement simultaneously algorithm based on step frequency waveforms[J]. Journal of Sichuan Ordnance, 2015, 36(5): 115-118. doi: 10.11809/scbgxb2015.05.03. XIA Guifen, SU Hongyan, and HUANG Peikang. Velocity compensation methods for LPRF modulated frequency stepped-frequency (MFSF) radar[J]. Journal of Systems Engineering and Electronics, 2010, 21(5): 746-751. doi: 10.3969/j.issn.1004-4132.2010.05.005. CHEN Yichang, LI Gang, ZHANG Qun, et al. Motion compensation for airborne SAR via parametric sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(1): 551-562. doi: 10.1109/TGRS. 2016.2611522. FAISAL Shabbir and PIOTR Omenzetter. Model updating using genetic algorithms with sequential niche technique[J]. Engineering Structures, 2016, 120: 166-182. doi: 10.1016/ j.engstruct.2016.04.028. DOWNEY Austin, HU Chao, and LAFLAMME Simon. Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool[J]. Structural Health Monitoring, 2017(10): 1475921717702537-1-1475921717702537-10, doi: 10.1177/ 1475921717702537. -
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