基于多幀數(shù)據(jù)聯(lián)合處理的機(jī)載單通道雷達(dá)貝葉斯前視成像
doi: 10.11999/JEIT150153
基金項目:
國家自然科學(xué)基金(61271297, 61272281, 61301284)和博士學(xué)科點科研專項基金(20110203110001)資助課題
Bayesian Forward-looking Imaging for Airborne Single-channel Radar Based on Combined Multiple Frames Data
Funds:
The National Natural Science Foundation of China (61271297, 61272281, 61301284)
-
摘要: 針對機(jī)載單通道雷達(dá)前視分辨率不高的問題,該文提出一種基于多幀數(shù)據(jù)聯(lián)合處理的貝葉斯前視成像方法。該文首先建立高斯背景下的前視回波信號模型,然后將散射場景的處理空間由單幀波束域的低維空間擴(kuò)展到多幀波束域聯(lián)合而成的高維空間以增加其空域稀疏性,并對散射場景的稀疏性進(jìn)行統(tǒng)計建模。最后基于貝葉斯理論,將前視條件下的雷達(dá)成像轉(zhuǎn)化為貝葉斯準(zhǔn)則下的優(yōu)化問題,并通過共軛梯度算法進(jìn)行優(yōu)化求解。在優(yōu)化求解時,稀疏統(tǒng)計參數(shù)從數(shù)據(jù)迭代過程中估計得到。仿真結(jié)果和實測數(shù)據(jù)表明該方法不僅可以對前視場景進(jìn)行高分辨成像,還可以抑制虛假散射點。
-
關(guān)鍵詞:
- 機(jī)載雷達(dá) /
- 實波束銳化 /
- 前視成像 /
- 貝葉斯準(zhǔn)則 /
- 超分辨
Abstract: An adaptive Bayesian super-resolution imaging algorithm based on the combined multiple frames data is proposed to enhance the azimuth resolution of airborne single-channel forward-looking radar. The echo of the forward-looking radar in the Gaussian noise is modeled, and the processing space is expanded from the low dimension of single frame data to the high dimension of multiple frames data to enhance the sparsity of domain scatterers. During the framework, the sparsity of the scatterers is modeled in spatial domain, and the imaging is converted into a problem of signal optimization based on Bayesian formalism. The final optimal result can be solved by the conjugate gradient method. In this framework, the statistic parameter is estimated with data-driven. Simulation results demonstrate that the proposed algorithm both can increase the resolution of the forward-looking imaging results and suppress the artifacts.-
Key words:
- Airborne radar /
- Real beam sharpening /
- Forward-looking imaging /
- Bayesian formalism /
- Super-resolution
-
Richards M A. Fundamentals of Radar Signal Processing [M]. New York: McGraw-Hill, 2005: 390-401. Wang R, Deng Y K, Loffeld O, et al.. Processing the azimuth-variant bistatic SAR data by using monostatic imaging algorithms based on 2-D principle of stationary phase[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3504-3520. Wu J, Li Z, Huang Y, et al.. Bistatic forward-looking SAR with stationary transmitter based on keystone transform and nonlinear chirp scaling[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 148-152. Wang R, Deng Y K, Zhang Z Z, et al.. Double-channel bistatic SAR system with spaceborne illuminator for 2-D and 3-D SAR remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(8): 4496-4507. Li W, Yang J, and Huang Y. Keystone transform-based space-variant range migration correction for airborne forward-looking scanning radar[J]. Electronics Letters, 2012, 48(2): 121-122. 包敏, 周鵬, 史林. 雙天線前視彈載SAR 解模糊算法研究[J]. 電子與信息學(xué)報, 2013, 35(12): 2857-2862. Bao Min, Zhou Peng, and Shi Lin. Study on deambiguity algorithm for double antenna forward looking missile borne SAR[J]. Journal of Electronic Information Technology, 2013, 35(12): 2857-2862. 龐礡, 王雪松, 代大海, 等. 基于數(shù)字波束銳化的高速前視合成孔徑雷達(dá)成像算法[J]. 電波科學(xué)學(xué)報, 2014, 29(1): 92-98. Pang Bo, Wang Xue-song, Dai Da-hai, et al.. Imaging algorithm of high velocity forward-looking SAR based on digital beam sharpening[J]. Chinese Journal of Radio Science, 2014, 29(1): 92-98. 吳迪, 朱岱寅, 田斌, 等. 單脈沖成像算法性能分析[J]. 航空學(xué)報, 2012, 33(10): 1905-1914. Wu Di, Zhu Dai-yin, Tian Bin, et al.. Performance evaluation for monopulse imaging algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2012, 33(10): 1905-1914. 楊志偉, 賀順, 廖桂生. 機(jī)載單通道雷達(dá)實波束掃描的前視探測[J]. 航空學(xué)報, 2012, 33(12): 2240-2245. Yang Zhi-wei, He Shun, and Liao Gui-sheng. Forward- looking detection for airborne single-channel radar with beam scanning[J]. Acta Aeronautica et Astronautica Sinica, 2012, 33(12): 2240-2245. 溫曉楊, 匡綱要, 胡杰民, 等. 基于實波束掃描的相控陣?yán)走_(dá)前視成像[J]. 航空學(xué)報, 2014, 35(7): 1977-1991. Wen Xiao-yang, Kuang Gang-yao, Hu Jie-min, et al.. Forward-looking imaging based on real beam scanning phased array radars[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(7): 1977-1991. 王軍, 趙宜楠, 喬曉林. 基于壓縮感知的雷達(dá)前視向稀疏目標(biāo)分解[J]. 電子與信息學(xué)報, 2014, 36(8): 1978-1984. Wang Jun, Zhao Yi-nan, and Qiao Xiao-lin. A sparse target-scenario determination strategy based on compressed sensing for active radar in the line of sight[J]. Journal of Electronic Information Technology, 2014, 36(8): 1978-1984. Richards M A. Iterative noncoherent angular superresolution [C]. Proceedings of the IEEE National Radar Conference, Ann Arbor, USA, 1988: 100-105. 李悅麗, 梁甸農(nóng), 黃曉濤. 一種單脈沖雷達(dá)多通道解卷積前視成像方法[J]. 信號處理, 2007, 23(5): 700-703. Li Yue-li, Liang Dian-nong, and Huang Xiao-tao. A multi-channel deconvolution based on forward-looking imaging method in monopulse radar[J]. Signal Processing, 2007, 23(5): 700-703. Li D Y, Huang Y L, and Yang J Y. Real beam radar imaging based on adaptive Lucy-Richardson algorithm[C]. Proceedings of the International Conference on Radar, Chengdu, China, 2011: 1437-1440. Babacan S D, Molina R, and Katsaggelos A K. Bayesian compressive sensing using Laplace priors[J]. IEEE Transactions on Image Processing, 2010, 19(1): 53-63. 夏建明, 楊俊安, 陳功. 參數(shù)自適應(yīng)調(diào)整的稀疏貝葉斯重構(gòu)算法[J]. 電子與信息學(xué)報, 2014, 36(6): 1355-1361. Xia Jian-ming, Yang Jun-an, and Chen Gong. Bayesian sparse reconstruction with adaptive parameters adjustment [J]. Journal of Electronic Information Technology, 2014, 36(6): 1355-1361. Xu Gang, Xing Meng-dao, Zhang Lei, et al.. Bayesian inverse synthetic aperture radar imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(6): 1150-1154. 吳敏, 邢孟道, 張磊. 基于壓縮感知的二維聯(lián)合超分辨ISAR成像算法[J]. 電子與信息學(xué)報, 2014, 36(1): 187-193. Wu Min, Xing Meng-dao, and Zhang Lei. Two dimensional joint super-resolution ISAR imaging algorithm based on compressive sensing[J]. Journal of Electronic Information Technology, 2014, 36(1): 187-193. Long Teng, Lu Zheng, Ding Ze-gang, et al.. A DBS Boppler centroid estimation algorithm based on entropy minimization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3703-3712. -
計量
- 文章訪問數(shù): 1517
- HTML全文瀏覽量: 140
- PDF下載量: 549
- 被引次數(shù): 0