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基于稀疏貝葉斯學(xué)習(xí)的多跳頻信號(hào)DOA估計(jì)方法

郭英,  東潤(rùn)澤,  張坤峰,  眭萍,  楊銀松

郭英, 東潤(rùn)澤, 張坤峰, 眭萍, 楊銀松. 基于稀疏貝葉斯學(xué)習(xí)的多跳頻信號(hào)DOA估計(jì)方法[J]. 電子與信息學(xué)報(bào), 2019, 41(3): 516-522. doi: 10.11999/JEIT180435
引用本文: 郭英, 東潤(rùn)澤, 張坤峰, 眭萍, 楊銀松. 基于稀疏貝葉斯學(xué)習(xí)的多跳頻信號(hào)DOA估計(jì)方法[J]. 電子與信息學(xué)報(bào), 2019, 41(3): 516-522. doi: 10.11999/JEIT180435
Ying GUO, Runze DONG, Kunfeng ZHANG, Ping SUI, Yinsong YANG. Direction of Arrival Estimation for Multiple Frequency Hopping Signals Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2019, 41(3): 516-522. doi: 10.11999/JEIT180435
Citation: Ying GUO, Runze DONG, Kunfeng ZHANG, Ping SUI, Yinsong YANG. Direction of Arrival Estimation for Multiple Frequency Hopping Signals Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2019, 41(3): 516-522. doi: 10.11999/JEIT180435

基于稀疏貝葉斯學(xué)習(xí)的多跳頻信號(hào)DOA估計(jì)方法

doi: 10.11999/JEIT180435
  • 1.

    空軍工程大學(xué)信息與導(dǎo)航學(xué)院 ??西安 ??710077

  • 2.

    通信網(wǎng)信息傳輸與分發(fā)技術(shù)重點(diǎn)實(shí)驗(yàn)室 ??石家莊 ??050081

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61601500)
詳細(xì)信息
    作者簡(jiǎn)介:

    郭英：女，1961年生，博士，教授，博士生導(dǎo)師，研究方向?yàn)橥ㄐ判盘?hào)處理、自適應(yīng)信號(hào)處理等

    東潤(rùn)澤：男，1995年生，碩士生，研究方向?yàn)樘l信號(hào)檢測(cè)、參數(shù)估計(jì)

    張坤峰：男，1989年生，博士生，研究方向?yàn)橥ㄐ判盘?hào)偵查處理、陣列信號(hào)處理

    眭萍：女，1991年生，博士生，研究方向?yàn)樾盘?hào)指紋特征識(shí)別

    楊銀松：男，1994年生，碩士生，研究方向?yàn)橥ㄐ判盘?hào)處理、跳頻信號(hào)網(wǎng)臺(tái)分選

    通訊作者:

    郭英　yguo@163.com

  • 中圖分類(lèi)號(hào): TN911.7

Direction of Arrival Estimation for Multiple Frequency Hopping Signals Based on Sparse Bayesian Learning

  • 1.

    Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

  • 2.

    Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, Shijiazhuang 050081, China

Funds: The National Natural Science Foundation of China (61601500)
  • 摘要:

    針對(duì)多跳頻信號(hào)空域參數(shù)估計(jì)問(wèn)題，該文在稀疏貝葉斯學(xué)習(xí)(SBL)的基礎(chǔ)上，利用跳頻信號(hào)的空域稀疏性實(shí)現(xiàn)了波達(dá)方向(DOA)的估計(jì)。首先構(gòu)造空域離散網(wǎng)格，將實(shí)際DOA與網(wǎng)格點(diǎn)之間的偏移量建模進(jìn)離散網(wǎng)格中，建立多跳頻信號(hào)均勻線(xiàn)陣接收數(shù)據(jù)模型；然后通過(guò)SBL理論得到行稀疏信號(hào)矩陣的后驗(yàn)概率分布，用超參數(shù)控制偏移量和信號(hào)矩陣的行稀疏程度；最后利用期望最大化(EM)算法對(duì)超參數(shù)進(jìn)行迭代，得到信號(hào)矩陣的最大后驗(yàn)估計(jì)以完成DOA的估計(jì)。理論分析與仿真實(shí)驗(yàn)表明該方法具有良好的估計(jì)性能并能適應(yīng)較少快拍數(shù)的情況。

    Abstract:

    To solve the problem of spatial parameter estimation of multi-frequency hopping signals, the sparsity in spatial domain of frequency hopping signals is used to realize the Direction Of Arrival (DOA) estimation based on Sparse Bayesian Learning (SBL). First, the spatial discrete grid is constructed and the offset between the actual DOA and the grid points is modeled into it. The data model of the uniform linear array with multiple frequency hopping signals is established. Then the posterior probability distribution of the sparse signal matrix is obtained by the SBL theory, and the line sparsity of the signal matrix and the offset is controlled by the hyperparameters. Finally, The expectation maximization algorithm is used to iterate the hyper parameters, and the maximum posteriori estimation of the signal matrix is obtained to complete the DOA estimation. Theoretical analysis and simulation experiments show that this method has good estimation performance and can adapt to less snapshots.

    • HTML全文

  • 圖  1  均勻線(xiàn)陣接收模型

    圖  2  不同陣元數(shù)下算法性能與運(yùn)行時(shí)間的比較

    圖  3  不同網(wǎng)格間隔下算法性能與運(yùn)行時(shí)間的比較

    圖  4  各算法的空間譜比較

    圖  5  不同快拍數(shù)下算法性能的比較

    表  1  不同快拍數(shù)下算法運(yùn)行時(shí)間的比較(s)

    快拍數(shù)2080
    本文算法所用時(shí)間0.38040.5915
    稀疏重構(gòu)算法所用時(shí)間0.40660.3935
    OGSBI算法所用時(shí)間0.64540.8428
    下載: 導(dǎo)出CSV
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  • 收稿日期:  2018-05-08
  • 修回日期:  2018-09-20
  • 網(wǎng)絡(luò)出版日期:  2018-10-23
  • 刊出日期:  2019-03-01

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