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)
基金項(xiàng)目:
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
Funds:
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. -
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