Domain-Adaptive Direction of Arrival (DOA) Estimation in Complex Indoor Environments Based on Convolutional Autoencoder and Transfer Learning

Direction of arrival (DOA) estimation for signal sources in indoor environments has become increasingly important in wireless communications and smart home applications. However, complex indoor conditions, such as multipath effects and noise interference, pose significant challenges to estimation ac...

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Published in:	Sensors (Basel, Switzerland) Vol. 25; no. 10; p. 2959
Main Authors:	Shen, Lingyu, Li, Jianfeng, Pan, Jingjing, Shi, Junpeng, Xu, Rui, Wang, Hao, Deng, Weiming
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 08.05.2025 MDPI
Subjects:	Adaptation Algorithms Datasets Deep Learning Design DOA domain adaptation indoor scenes Location-based systems Machine learning Methods Mobile communication systems Neural networks Signal processing Support vector machines Wireless communication systems China domain adaptation Deep Learning indoor scenes DOA
ISSN:	1424-8220, 1424-8220
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Abstract	Direction of arrival (DOA) estimation for signal sources in indoor environments has become increasingly important in wireless communications and smart home applications. However, complex indoor conditions, such as multipath effects and noise interference, pose significant challenges to estimation accuracy. This issue is further complicated by domain discrepancies in data collected from different environments. To address these challenges, we propose a deep domain-adaptation-based DOA estimation method. The approach begins with deep feature extraction using a Convolutional Autoencoder (CAE) and employs a Domain-Adversarial Neural Network (DANN) for domain adaptation. By integrating Gradient Reversal Layer (GRL) and Maximum Mean Discrepancy (MMD) loss functions, the model effectively reduces distributional differences between the source and target domains. The CAE-DANN enables transfer learning between data with similar features from different domains. With minimal labeled data from the target domain incorporated into the source domain, the model leverages labeled source data to adapt to unlabeled target data. GRL counters domain shifts, while MMD refines feature alignment. Experimental results show that, in complex indoor environments, the proposed method outperforms other methods in terms of overall DOA prediction performance in both the source and target domains. This highlights a robust and practical solution for high-precision DOA estimation in new environments, requiring minimal labeled data.
AbstractList	Direction of arrival (DOA) estimation for signal sources in indoor environments has become increasingly important in wireless communications and smart home applications. However, complex indoor conditions, such as multipath effects and noise interference, pose significant challenges to estimation accuracy. This issue is further complicated by domain discrepancies in data collected from different environments. To address these challenges, we propose a deep domain-adaptation-based DOA estimation method. The approach begins with deep feature extraction using a Convolutional Autoencoder (CAE) and employs a Domain-Adversarial Neural Network (DANN) for domain adaptation. By integrating Gradient Reversal Layer (GRL) and Maximum Mean Discrepancy (MMD) loss functions, the model effectively reduces distributional differences between the source and target domains. The CAE-DANN enables transfer learning between data with similar features from different domains. With minimal labeled data from the target domain incorporated into the source domain, the model leverages labeled source data to adapt to unlabeled target data. GRL counters domain shifts, while MMD refines feature alignment. Experimental results show that, in complex indoor environments, the proposed method outperforms other methods in terms of overall DOA prediction performance in both the source and target domains. This highlights a robust and practical solution for high-precision DOA estimation in new environments, requiring minimal labeled data. Direction of arrival (DOA) estimation for signal sources in indoor environments has become increasingly important in wireless communications and smart home applications. However, complex indoor conditions, such as multipath effects and noise interference, pose significant challenges to estimation accuracy. This issue is further complicated by domain discrepancies in data collected from different environments. To address these challenges, we propose a deep domain-adaptation-based DOA estimation method. The approach begins with deep feature extraction using a Convolutional Autoencoder (CAE) and employs a Domain-Adversarial Neural Network (DANN) for domain adaptation. By integrating Gradient Reversal Layer (GRL) and Maximum Mean Discrepancy (MMD) loss functions, the model effectively reduces distributional differences between the source and target domains. The CAE-DANN enables transfer learning between data with similar features from different domains. With minimal labeled data from the target domain incorporated into the source domain, the model leverages labeled source data to adapt to unlabeled target data. GRL counters domain shifts, while MMD refines feature alignment. Experimental results show that, in complex indoor environments, the proposed method outperforms other methods in terms of overall DOA prediction performance in both the source and target domains. This highlights a robust and practical solution for high-precision DOA estimation in new environments, requiring minimal labeled data.Direction of arrival (DOA) estimation for signal sources in indoor environments has become increasingly important in wireless communications and smart home applications. However, complex indoor conditions, such as multipath effects and noise interference, pose significant challenges to estimation accuracy. This issue is further complicated by domain discrepancies in data collected from different environments. To address these challenges, we propose a deep domain-adaptation-based DOA estimation method. The approach begins with deep feature extraction using a Convolutional Autoencoder (CAE) and employs a Domain-Adversarial Neural Network (DANN) for domain adaptation. By integrating Gradient Reversal Layer (GRL) and Maximum Mean Discrepancy (MMD) loss functions, the model effectively reduces distributional differences between the source and target domains. The CAE-DANN enables transfer learning between data with similar features from different domains. With minimal labeled data from the target domain incorporated into the source domain, the model leverages labeled source data to adapt to unlabeled target data. GRL counters domain shifts, while MMD refines feature alignment. Experimental results show that, in complex indoor environments, the proposed method outperforms other methods in terms of overall DOA prediction performance in both the source and target domains. This highlights a robust and practical solution for high-precision DOA estimation in new environments, requiring minimal labeled data.
Audience	Academic
Author	Xu, Rui Deng, Weiming Li, Jianfeng Shi, Junpeng Pan, Jingjing Shen, Lingyu Wang, Hao
AuthorAffiliation	College of Electronic and Information Engineering, Nanjing University of Aeronautics and Asteonautics, Nanjing 211106, China; shenlingyu@nuaa.edu.cn (L.S.); jingjingpan@nuaa.edu.cn (J.P.); shijunpeng20@nudt.edu.cn (J.S.); rey.xu@nuaa.edu.cn (R.X.); wanghao319@nuaa.edu.cn (H.W.); dengweiming@nuaa.edu.cn (W.D.)
AuthorAffiliation_xml	– name: College of Electronic and Information Engineering, Nanjing University of Aeronautics and Asteonautics, Nanjing 211106, China; shenlingyu@nuaa.edu.cn (L.S.); jingjingpan@nuaa.edu.cn (J.P.); shijunpeng20@nudt.edu.cn (J.S.); rey.xu@nuaa.edu.cn (R.X.); wanghao319@nuaa.edu.cn (H.W.); dengweiming@nuaa.edu.cn (W.D.)
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SubjectTerms	Adaptation Algorithms Datasets Deep Learning Design DOA domain adaptation indoor scenes Location-based systems Machine learning Methods Mobile communication systems Neural networks Signal processing Support vector machines Wireless communication systems
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Title	Domain-Adaptive Direction of Arrival (DOA) Estimation in Complex Indoor Environments Based on Convolutional Autoencoder and Transfer Learning
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