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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

陳洪猛,  李明,  王澤玉,  盧云龍,  張鵬,  左磊

陳洪猛, 李明, 王澤玉, 盧云龍, 張鵬, 左磊. 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[J]. 電子與信息學(xué)報, 2015, 37(10): 2328-2334. doi: 10.11999/JEIT150153
引用本文: 陳洪猛, 李明, 王澤玉, 盧云龍, 張鵬, 左磊. 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[J]. 電子與信息學(xué)報, 2015, 37(10): 2328-2334. doi: 10.11999/JEIT150153
Chen Hong-meng, Li Ming, Wang Ze-Yu, Lu Yun-long, Zhang Peng, Zuo Lei. Bayesian Forward-looking Imaging for Airborne Single-channel Radar Based on Combined Multiple Frames Data[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2328-2334. doi: 10.11999/JEIT150153
Citation: Chen Hong-meng, Li Ming, Wang Ze-Yu, Lu Yun-long, Zhang Peng, Zuo Lei. Bayesian Forward-looking Imaging for Airborne Single-channel Radar Based on Combined Multiple Frames Data[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2328-2334. doi: 10.11999/JEIT150153

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。
    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.
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出版歷程
  • 收稿日期:  2015-01-27
  • 修回日期:  2015-04-20
  • 刊出日期:  2015-10-19

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