{"title":"通过随机森林虚拟阵列扩展进行高分辨率 DOA 估算","authors":"","doi":"10.1016/j.aeue.2024.155446","DOIUrl":null,"url":null,"abstract":"<div><p>In sonar detection, underwater recognition, and similar fields, the challenge of using small-scale arrays is frequently encountered. With small-scale arrays, the degrees of freedom (DOFs) and accuracy of direction of arrival (DOA) estimation decrease significantly. Moreover, existing algorithms are less robust in complex environments and typically require substantial computing resources to ensure accurate estimation. To overcome these problems, this paper proposes a high-resolution DOA estimation via Random Forest virtual array extension (RFVAE). The algorithm first trains the random forest (RF) network using real array element data, and thus proposes a virtual covariance reconstruction technique. This technique allows for a substantial increase in the number of virtual array elements. Then, based on the virtual covariance reconstruction technique, a high resolution estimation network technique is proposed, which can increase the accuracy and DOFs of DOA estimation. Experimental results show that the proposed algorithm reduces error by 30% to 80% compared to recent technologies and can provide up to twice as many virtual elements. Additionally, the estimation speed can be increased by 3 to 1000 times, and it has significantly stronger robustness, functioning normally at signal-to-noise ratios 10 dB lower than the conditions under which other algorithms fail.</p></div>","PeriodicalId":50844,"journal":{"name":"Aeu-International Journal of Electronics and Communications","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A high-resolution DOA estimation via random forest virtual array extension\",\"authors\":\"\",\"doi\":\"10.1016/j.aeue.2024.155446\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In sonar detection, underwater recognition, and similar fields, the challenge of using small-scale arrays is frequently encountered. With small-scale arrays, the degrees of freedom (DOFs) and accuracy of direction of arrival (DOA) estimation decrease significantly. Moreover, existing algorithms are less robust in complex environments and typically require substantial computing resources to ensure accurate estimation. To overcome these problems, this paper proposes a high-resolution DOA estimation via Random Forest virtual array extension (RFVAE). The algorithm first trains the random forest (RF) network using real array element data, and thus proposes a virtual covariance reconstruction technique. This technique allows for a substantial increase in the number of virtual array elements. Then, based on the virtual covariance reconstruction technique, a high resolution estimation network technique is proposed, which can increase the accuracy and DOFs of DOA estimation. Experimental results show that the proposed algorithm reduces error by 30% to 80% compared to recent technologies and can provide up to twice as many virtual elements. Additionally, the estimation speed can be increased by 3 to 1000 times, and it has significantly stronger robustness, functioning normally at signal-to-noise ratios 10 dB lower than the conditions under which other algorithms fail.</p></div>\",\"PeriodicalId\":50844,\"journal\":{\"name\":\"Aeu-International Journal of Electronics and Communications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Aeu-International Journal of Electronics and Communications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1434841124003327\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aeu-International Journal of Electronics and Communications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1434841124003327","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
在声纳探测、水下识别和类似领域,经常会遇到使用小规模阵列的挑战。使用小规模阵列时,自由度(DOFs)和到达方向(DOA)估计的精确度会大大降低。此外,现有算法在复杂环境中的鲁棒性较差,通常需要大量计算资源才能确保估算准确。为了克服这些问题,本文提出了一种通过随机森林虚拟阵列扩展(RFVAE)进行高分辨率 DOA 估计的方法。该算法首先使用真实阵元数据训练随机森林(RF)网络,从而提出了一种虚拟协方差重构技术。该技术可大幅增加虚拟阵元的数量。然后,在虚拟协方差重建技术的基础上,提出了一种高分辨率估计网络技术,可以提高 DOA 估计的精度和 DOF。实验结果表明,与最新技术相比,所提出的算法可减少 30% 至 80% 的误差,并能提供多达两倍的虚拟元素。此外,估计速度可提高 3 到 1000 倍,而且鲁棒性明显增强,在信噪比比其他算法失效条件低 10 dB 的情况下也能正常工作。
A high-resolution DOA estimation via random forest virtual array extension
In sonar detection, underwater recognition, and similar fields, the challenge of using small-scale arrays is frequently encountered. With small-scale arrays, the degrees of freedom (DOFs) and accuracy of direction of arrival (DOA) estimation decrease significantly. Moreover, existing algorithms are less robust in complex environments and typically require substantial computing resources to ensure accurate estimation. To overcome these problems, this paper proposes a high-resolution DOA estimation via Random Forest virtual array extension (RFVAE). The algorithm first trains the random forest (RF) network using real array element data, and thus proposes a virtual covariance reconstruction technique. This technique allows for a substantial increase in the number of virtual array elements. Then, based on the virtual covariance reconstruction technique, a high resolution estimation network technique is proposed, which can increase the accuracy and DOFs of DOA estimation. Experimental results show that the proposed algorithm reduces error by 30% to 80% compared to recent technologies and can provide up to twice as many virtual elements. Additionally, the estimation speed can be increased by 3 to 1000 times, and it has significantly stronger robustness, functioning normally at signal-to-noise ratios 10 dB lower than the conditions under which other algorithms fail.
期刊介绍:
AEÜ is an international scientific journal which publishes both original works and invited tutorials. The journal''s scope covers all aspects of theory and design of circuits, systems and devices for electronics, signal processing, and communication, including:
signal and system theory, digital signal processing
network theory and circuit design
information theory, communication theory and techniques, modulation, source and channel coding
switching theory and techniques, communication protocols
optical communications
microwave theory and techniques, radar, sonar
antennas, wave propagation
AEÜ publishes full papers and letters with very short turn around time but a high standard review process. Review cycles are typically finished within twelve weeks by application of modern electronic communication facilities.