基于同步壓縮小波變換的主信號(hào)抑制技術(shù)

吳龍文; 牛金鵬; 王昭; 何勝陽(yáng); 趙雅琴

doi:10.11999/JEIT190650

基于同步壓縮小波變換的主信號(hào)抑制技術(shù)

doi: 10.11999/JEIT190650 cstr: 32379.14.JEIT190650

1.
哈爾濱工業(yè)大學(xué)電子與信息工程學(xué)院哈爾濱 150001
2.
中國(guó)電子科技集團(tuán)公司第二十九研究所成都 610036

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61671185)

詳細(xì)信息

作者簡(jiǎn)介:
吳龍文：男，1988年生，工程師，研究方向?yàn)檩椛湓磦€(gè)體識(shí)別

牛金鵬：男，1997年生，碩士生，研究方向?yàn)檩椛湓磦€(gè)體識(shí)別

王昭：男，1995年生，工程師，研究方向?yàn)檩椛湓磦€(gè)體識(shí)別

何勝陽(yáng)：男，1983年生，高級(jí)工程師，研究方向?yàn)闊o(wú)線光通信

趙雅琴：女，1976年生，教授，研究方向?yàn)檩椛湓醋R(shí)別和光通信

通訊作者:
趙雅琴　yaqinzhao@hit.edu.cn

中圖分類號(hào): TN971
計(jì)量
- 文章訪問(wèn)數(shù): 1882
- HTML全文瀏覽量: 1457
- PDF下載量: 70
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-08-28
- 修回日期: 2020-05-05
- 網(wǎng)絡(luò)出版日期: 2020-05-17
- 刊出日期: 2020-08-18

Primary Signal Suppression Based on Synchrosqueezed Wavelet Transform

1.
School of Electronics & Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2.
The 29th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China

Funds: The National Natural Science Foundation of China (61671185)

摘要

摘要:
在輻射源個(gè)體識(shí)別(SEI)技術(shù)中，能量較高的主信號(hào)往往導(dǎo)致微弱個(gè)體特征穩(wěn)定性降低，進(jìn)而影響最終的個(gè)體識(shí)別效果。為了解決該問(wèn)題并提升輻射源個(gè)體識(shí)別性能，該文提出基于同步壓縮小波變換的主信號(hào)抑制技術(shù)。首先，利用靜態(tài)小波變換完成對(duì)帶噪信號(hào)的去噪預(yù)處理；然后，利用同步壓縮小波變換完成對(duì)主信號(hào)的檢測(cè)和抑制，并以均方根誤差和皮爾遜相關(guān)系數(shù)為數(shù)值指標(biāo)，驗(yàn)證算法的有效性；最后，在主信號(hào)抑制的基礎(chǔ)上，利用分形理論中盒維數(shù)完成對(duì)信號(hào)的特征提取，并利用單核支持向量機(jī)驗(yàn)證個(gè)體識(shí)別性能。實(shí)驗(yàn)結(jié)果表明，與主信號(hào)抑制之前相比，主信號(hào)抑制算法下個(gè)體識(shí)別率提升了10%左右，驗(yàn)證了同步壓縮小波變換的主信號(hào)抑制算法對(duì)輻射源個(gè)體識(shí)別率提升的有效性。
- 輻射源個(gè)體識(shí)別 /
- 主信號(hào)抑制 /
- 同步壓縮小波變換 /
- 特征提取
Abstract:
In Specific Emitter Identification (SEI), the stability of individual features and final correct identification rate are always declined due to the influence of the primary signal with high energy on the individual features. To solve the problem above, a primary signal suppression algorithm based on synchrosqueezed wavelet transform is exploited for specific emitter identification in this paper. Firstly, a denoising method based on stationary wavelet transform is applied to preprocess the noised signal; Then, the detection and suppression of the primary signal from time-frequency distribution are developed, where root mean square error and Pearson correlation coefficient are used as numerical indicators to measure the effectiveness of the proposed primary signal suppression algorithm; Finally, a feature extraction based on box-counting dimension and a classification based on support vector machine are exploited to verify the identification performance. The simulation results show that the correct identification rate of SEI using the proposed primary signal suppression outperforms the conventional SEI with 10%, which proves the practical improvement of the proposed primary signal suppression algorithm on specific emitter identification.
- Specific Emitter Identification(SEI) /
- Primary signal suppression /
- Synchrosqueezed wavelet transform /
- Feature extraction

HTML全文

圖 1 SWT分解過(guò)程示意圖

下載: 全尺寸圖片幻燈片

圖 2 LFM信號(hào)下SST主信號(hào)抑制效果仿真

下載: 全尺寸圖片幻燈片

圖 3 SST主信號(hào)抑制仿真(擴(kuò)大相位噪聲頻1偏后)

下載: 全尺寸圖片幻燈片

圖 4 LFM信號(hào)源個(gè)體分形盒維數(shù)特征識(shí)別結(jié)果

下載: 全尺寸圖片幻燈片

圖 5 實(shí)測(cè)數(shù)據(jù)特征規(guī)范化后特征分布

下載: 全尺寸圖片幻燈片

表 1 加性相位噪聲參數(shù)

輻射源個(gè)體	與頻偏對(duì)應(yīng)的相位噪聲幅度(信相噪比(dB))
	f₁=±2.75 MHz	f₂=±2.80 MHz	f₃=±3.10 MHz
E₁	11.9897	12.7815	15.7918
E₂	10.4845	11.6722	16.1877
	f₂₁=±2.8 MHz	f₂₂=±2.9 MHz	f₂₃=±3.15 MHz
E₃	12.7815	14.0308	16.1394

下載: 導(dǎo)出CSV

表 2 實(shí)測(cè)數(shù)據(jù)特征結(jié)構(gòu)與來(lái)源

特征序號(hào)	特征來(lái)源
1～4	RF-DNA^[19]
4～8	IMF-DNA^[20]
9～12	BCD^[18]
13～20	SIB^[21]

下載: 導(dǎo)出CSV

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