基于子区域的宽带纯幅测向系统机器学习

G. R. Friedrichs, M. Elmansouri, D. Filipović
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引用次数: 0

摘要

近年来,对改进的计算资源的访问增加了对基于机器学习(ML)系统的实际部署的兴趣。ML被考虑的一个领域是测向(DF)[1]。通常,用于DF的ML方法利用预处理或特征提取。这产生了很好的结果,但它需要额外的计算,这增加了DF估计时间。传统的DF方法通常使用相关方法,其中接收到的信号与查找表中预先保存的转向向量相关。一些基于机器学习的方法,如本研究中提出的神经网络,可以保持比基线相关方法小得多的占用空间,并且省略了特征提取的需要。通常,这些系统部署多个并行估计器,它们单独覆盖整个视场(FOV)的一小部分,但总体上估计系统的整个感兴趣区域。这项工作提出了一个案例研究,以量化限制每个子区域估计器运行的视场(FOV)的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Subregion-Based Machine Learning for Wideband Amplitude-Only Direction-Finding Systems
In recent years, access to improved computing resources has contributed to the increased interest in the practical deployment of machine learning (ML) based systems. One area where ML has been considered is direction finding (DF) [1] . Typically, ML approaches for DF utilize preprocessing, or feature extraction. This produces good results, but it requires additional computations which increase the DF estimation time. Conventional DF approaches typically use the correlation method, where the received signal is correlated with pre-saved steering vectors in lookup tables. Some ML-based methods, such as the neural network presented in this work, can maintain a much smaller footprint than the baseline correlation approach, and omit the need for feature extraction. Often, these systems deploy multiple, parallel estimators which individually cover a fraction of the entire field-of-view (FOV), but collectively estimate over the system’s entire region of interest. This work presents a case study to quantify the impact of restricting the field-of-view (FOV) over which each subregion estimator operates.
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