基于分?jǐn)?shù)階傅里葉變換的窄帶雷達(dá)飛機(jī)目標(biāo)回波特征提取方法
doi: 10.11999/JEIT161035 cstr: 32379.14.JEIT161035
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61271024, 61322103)，高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金博士生導(dǎo)師類基金(20130203110013)，陜西省自然科學(xué)基礎(chǔ)研究計(jì)劃(2015JZ016)
Feature Extraction Method of Narrow-band Radar Airplane Signatures Based on Fractional Fourier Transform
Funds:
The National Natural Science Foundation of China (61271024, 61322103), The Foundation for Doctoral Supervisor of China (20130203110013), The Natural Science Foundation of Shaanxi Province (2015JZ016)
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摘要: 該文研究了常規(guī)窄帶雷達(dá)體制下，利用分?jǐn)?shù)階傅里葉變換擴(kuò)展特征域，從而解決直升機(jī)、螺旋槳飛機(jī)和噴氣式飛機(jī)3類飛機(jī)目標(biāo)回波分類中的特征提取問(wèn)題。在現(xiàn)代戰(zhàn)場(chǎng)中，直升機(jī)、螺旋槳飛機(jī)和噴氣式飛機(jī)具有不同的機(jī)動(dòng)性能，并各自承擔(dān)著重要的任務(wù)。因此，實(shí)現(xiàn)這3類飛機(jī)的分類具有重大的意義。該文針對(duì)3類飛機(jī)目標(biāo)分類的傳統(tǒng)特征數(shù)目少，包含信息量有限，導(dǎo)致分類性能不夠好的問(wèn)題，基于現(xiàn)有的特征提取方法引入分?jǐn)?shù)階傅里葉變換(Fractional Fourier Transform, FrFT)，在經(jīng)過(guò)FrFT后的分?jǐn)?shù)域提取3類飛機(jī)目標(biāo)回波的分?jǐn)?shù)階特征，彌補(bǔ)傳統(tǒng)特征的不足。并利用線性相關(guān)向量機(jī)(Relevance Vector Machine, RVM)的特征選擇功能對(duì)提取的分?jǐn)?shù)階特征進(jìn)行特征選擇并分類?；诜抡婧蛯?shí)測(cè)數(shù)據(jù)的實(shí)驗(yàn)結(jié)果證明該文提出的分?jǐn)?shù)階特征的分類性能較傳統(tǒng)時(shí)域、多普勒域特征有較大提升。
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關(guān)鍵詞:
- 窄帶雷達(dá) /
- 分?jǐn)?shù)階傅里葉變換 /
- 特征提取 /
- 特征選擇 /
- 目標(biāo)分類 /
- 噴氣引擎調(diào)制
Abstract: This paper studies on the feature extraction methods for the classification of helicopter, propeller-driven aircraft, and turbojet using a conventional narrow-band radar system. In the modern battlefield, the helicopter, propeller aircraft and jet aircraft with different motor performances each bear an important task. But the classification performance of the traditional features for the three types of aircraft target classification is not good enough, so the Fractional Fourier Transform (FrFT) is introduced. Based on the existing feature extraction method, the fractional order features of three kinds of aircraft targets are extracted from the fractional domain after FrFT to extend feature domain. Then, the effective features are selected from all extracted features and the classification of the three categories via linear Relevance Vector Machine (RVM) is realized. The experiments demonstrate that the proposed fractional features can improve the classification performance in comparison with some existing features from the time-domain and Doppler-frequency domain. -
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