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基于深度學(xué)習的智能化高精度測向方法
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中國電子科技集團公司第五十四研究所

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TN959

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國家自然科學(xué)基金(U19B2028)


Intelligent high accuracy direction finding method based on deep learning
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    摘要:

    提出了一種基于深度學(xué)習的智能化高精度快速波達方向(DOA)估計算法,根據神經(jīng)網(wǎng)絡(luò )通過(guò)數據驅動(dòng)而不依賴(lài)陣列流型的特點(diǎn),設計了基于卷積神經(jīng)網(wǎng)絡(luò )的PhaseDOA-Net回歸網(wǎng)絡(luò )模型實(shí)現估計算法,引入特定模塊對輸入信號進(jìn)行特征提取和處理,提高網(wǎng)絡(luò )模型的擬合效果,用所提網(wǎng)絡(luò )模型自主學(xué)習相位差矩陣與DOA之間的映射關(guān)系;引入殘差網(wǎng)絡(luò )結構,解決了卷積神經(jīng)網(wǎng)絡(luò )層數加深導致網(wǎng)絡(luò )退化的問(wèn)題;仿真生成了具有噪聲與幅相誤差的信號數據集,并構建信號相位差矩陣作為輸入;仿真結果表明,本算法可以提供更高精度的估計性能,大幅減小了估計時(shí)間,解決了現有方法在陣列模型誤差條件下無(wú)法準確得到DOA結果的問(wèn)題;通過(guò)基于實(shí)際信號環(huán)境中采集數據的訓練與測試,驗證了系統對不同噪聲、幅相誤差的魯棒性以及對不同信號頻率更好的適應能力。

    Abstract:

    An intelligent high accuracy fast direction of arrival(DOA)estimation algorithm based on deep learning is proposed. According to the characteristics of neural network driven by data and independent of array flow pattern,the PhaseDOA-Net regression network model based on convolutional neural network(CNN)is designed, and the residual network structure is introduced to solve the problem of network degradation caused by layer deepening of CNN.Specific modules are designed to extract and process the festures of the input signals,which improves the fitting effect of the networt model.The proposed network model is used to autonomously learn the mapping relationship between the phase difference matrix and DOA.The residual network structure is introduced to solve the problem of network degradation caused by layer deepening of CNN.The data sets with noise and amplitued-phase errors is generated by simulation,and the signal phase difference matrix is constructed as network input.The simulation results show that the algorithm can provide higher accuracy estimation performance, greatly reduce the estimation time, and solve the problems of the existing methods which cannot accurately obtain DOA results under the condition of array model error.Through the training and testing based on the collected data in the actual signal environment,the robustness of the system to different noises,amplitude-phase errors and the great adaptability to different signal frequencies are verified.

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趙偉豪,張君毅,李淳.基于深度學(xué)習的智能化高精度測向方法計算機測量與控制[J].,2023,31(1):15-21.

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  • 收稿日期:2022-10-13
  • 最后修改日期:2022-10-26
  • 錄用日期:2022-10-26
  • 在線(xiàn)發(fā)布日期: 2023-01-16
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