基于集成固有時間尺度分解的IFF輻射源個體識別算法

張玉; 李天琪; 張進; 唐波

doi:10.11999/JEIT190085

基于集成固有時間尺度分解的IFF輻射源個體識別算法

doi: 10.11999/JEIT190085

國防科技大學電子對抗學院合肥 230037

基金項目: 國家自然科學基金(61671453)，安徽省自然科學基金(1608085MF123)，國防科技大學自然科學基金(ZK18-03-19)

詳細信息

作者簡介:
張玉：男，1962年生，教授，碩士生導師，研究方向為雷達與通信中的信號處理

李天琪：女，1994年生，碩士生，研究方向為信號與信息處理

張進：男，1974年生，講師，研究方向為陣列信號處理

唐波：男，1985年生，副教授，研究方向為自適應(yīng)陣列信號處理、雷達波形設(shè)計等

通訊作者:
李天琪　helen_0370@163.com

中圖分類號: TN958.96
計量
- 文章訪問數(shù): 2542
- HTML全文瀏覽量: 1140
- PDF下載量: 84
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-01-28
- 修回日期: 2019-03-20
- 網(wǎng)絡(luò)出版日期: 2019-09-27
- 刊出日期: 2020-02-19

Individual Recognition Algorithm of IFF Radiation Sources Based on Ensemble Intrinsic Time-scale Decomposition

College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, China

Funds: The National Natural Science Foundation of China (61671453), The Natural Science Foundation of Anhui Province (1608085MF123), The Natural Science Foundation of National University of Defense Technology (ZK18-03-19)

摘要

摘要:
為研究敵我識別(IFF)輻射源信號的細微特征，針對目前在復(fù)雜噪聲環(huán)境中IFF輻射源個體識別研究不足的問題，該文提出一種基于集成固有時間尺度分解的IFF輻射源個體識別算法。該算法應(yīng)用集成固有時間尺度分解(EITD)將采樣信號自適應(yīng)劃分為若干有實際意義的信號分量并求取IFF輻射源信號在時頻域的能量分布圖。通過對時頻能量譜的紋理分析，以圖像的紋理特征表征輻射源信號的無意調(diào)制特征，送入支持向量機(SVM)中進行分類識別。實驗表明，所提算法相較于基于希爾伯特-黃變換(HHT)、基于固有時間尺度分解(ITD)的輻射源個體識別方法在識別準確度上有較大提升。
- 圖像處理 /
- 敵我識別 /
- 輻射源個體識別 /
- 時頻分析
Abstract:
In order to study the subtle feature recognition of Identification Foe or Friend (IFF) radiation source signals, this paper proposes an IFF individual recognition method based on ensemble intrinsic time-scale decomposition to solve the problem of insufficient research on individual identification of IFF radiation source in complex noise environment. In this algorithm, the Ensemble Intrinsic Time-scale Decomposition (EITD) is applied to dividing the sampled signals into several practical signal components and obtaining the energy distribution diagram of the IFF radiation source signals in time-frequency domain. Through the texture analysis of time-frequency energy spectrum, the unintentional modulation feature of the radiation source signals is represented by the texture features of the image, which are sent to the Support Vector Machine (SVM) for classification and recognition. Experiments show that the proposed method is more accurate than the Hilbert-Huang Transform (HHT) and Inherent Time scale Decomposition (ITD) based method.
- Image processing /
- Identification Foe or Friend (IFF) /
- Specific emitter identification /
- Time-frequency analysis

HTML全文

圖 1 本文算法流程圖

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圖 2 原始信號及EITD分解結(jié)果

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圖 3 索貝爾算子圖例

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圖 4 不同輻射源信號的時頻能量譜

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圖 5 隨信噪比變化時仿真信號的識別性能對比

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圖 6 不同時頻分析手段對識別率的影響

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圖 7 訓練樣本個數(shù)對識別性能的影響

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表 1 各方法的平均運算時間(ms)

方法 ITD EMD EITD EITD (迭代次數(shù)5)

運算時間 8.4 19.0 21.3 10.2

下載: 導出CSV

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