基于隨機(jī)調(diào)頻步進(jìn)信號(hào)的高分辨ISAR成像方法

呂明久; 李少東; 楊軍; 馬曉巖

doi:10.11999/JEIT160177

基于隨機(jī)調(diào)頻步進(jìn)信號(hào)的高分辨ISAR成像方法

doi: 10.11999/JEIT160177

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61671469)

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- 文章訪問(wèn)數(shù): 1348
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-03-01
- 修回日期: 2016-09-01
- 刊出日期: 2016-12-19

High Resolution ISAR Imaging Method Based on Random Chirp Frequency-stepped Signal

Funds:

The National Natural Science Foundation of China (61671469)

摘要

摘要: 為充分利用隨機(jī)調(diào)頻步進(jìn)逆合成孔徑雷達(dá)回波所具有的聯(lián)合稀疏特征，提高成像性能，該文提出一種基于分布式壓縮感知理論的隨機(jī)調(diào)頻步進(jìn)逆合成孔徑雷達(dá)高分辨成像方法。首先構(gòu)建隨機(jī)調(diào)頻步進(jìn)信號(hào)回波的聯(lián)合稀疏表示模型，并完成子脈沖的脈沖壓縮處理；其次，基于每組子脈沖的隨機(jī)方式(組與組之間的隨機(jī)方式不同)，構(gòu)建相應(yīng)的隨機(jī)量測(cè)矩陣，獲取回波的壓縮感知信號(hào)模型，并利用分布式壓縮感知理論實(shí)現(xiàn)距離向聯(lián)合高分辨重構(gòu)；最后結(jié)合回波在方位向的稀疏性，采用快速稀疏重構(gòu)算法實(shí)現(xiàn)方位向高分辨成像。理論分析和仿真結(jié)果表明由于充分利用了隨機(jī)調(diào)頻步進(jìn)信號(hào)回波的隨機(jī)性與聯(lián)合稀疏特征，所提出方法具有重構(gòu)精度高、距離向采樣率低、抗噪性能強(qiáng)等特點(diǎn)。
- 逆合成孔徑雷達(dá)成像 /
- 聯(lián)合稀疏模型 /
- 分布式壓縮感知 /
- 隨機(jī)調(diào)頻步進(jìn)信號(hào)
Abstract: In order to make full use of the joint sparse physical characteristics of the radar echo to improve imaging performance. A novel super resolution Inverse SAR (ISAR) imaging method based on distributed compressed sensing theory is proposed. Firstly, the joint sparse echo model of the random chirp frequency-stepped signal is built and the pulse compression processing of each sub-pulse is processed. Secondly, owing to different random patterns of each group, different measurement matrices are constructed in accordance with the random pattern of sub-pulse signal. Then the corresponding compressed sensing model of the echo is built and the supper resolution range profile is obtained via the distributed compressed sensing theory. Finally, the supper resolution inverse synthetic aperture radar image can be obtained by a fast compressed sensing reconstruction algorithm, which is used to achieve the high resolution reconstruction in azimuth direction based on the sparse features. Theoretical analysis and simulation results show that the proposed method has the characteristics of high reconstruction accuracy, low sampling rate and strong anti-noise performance.
- Inverse SAR (ISAR) imaging /
- Joint sparsity model /
- Distributed compressed sensing /
- Random chirp frequency-stepped signal

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