A Support Vector Regression-Based Method for Target Direction of Arrival Estimation From HF Radar Data

Wang, Ruokun; Wen, Biyang; Huang, Weimin

doi:10.1109/lgrs.2018.2807405

Cited by 42 publications

(22 citation statements)

References 22 publications

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“…Wang et al [7] pointed out that the performance of MUSIC algorithm relied on the precision of the antenna pattern which could be contaminated by nearby 2 Mathematical Problems in Engineering electromagnetic interference and was not easy to measure, and therefore it was meaningful to seek a direction finding approach without the need of precise measurement of the antenna pattern. ey developed a SVR-based direction finding method for an M-element radar receiving system.…”

Section: Concrete Scenariosmentioning

confidence: 99%

A Review on Machine Learning-Based Radio Direction Finding

You¹,

Lu²,

Ye³

et al. 2020

Mathematical Problems in Engineering

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Section: Concrete Scenariosmentioning

confidence: 99%

A Review on Machine Learning-Based Radio Direction Finding

You¹,

Lu²,

Ye³

et al. 2020

Mathematical Problems in Engineering

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“…How to improve the performance of direction of arrival (DOA) estimation, many experts and scholars have made a great deal of research. In general, there are three categories of array signal processing approaches to solving the problem, high‐resolution techniques [5–9], maximum‐likelihood (ML) techniques [10–13] and neural network techniques [14–21].…”

Section: Introductionmentioning

confidence: 99%

“…A multilayer feedforward neural network is used to estimate geophysical profiles and consequently yields better retrieval accuracies. In [20], a support vector regression‐based method is used to estimate DOA for high‐frequency radar.…”

Section: Introductionmentioning

confidence: 99%

Altitude measurement based on characteristics reversal by deep neural network for VHF radar

Xiang

Chen

Yang

et al. 2019

IET Radar, Sonar & Navigation

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“…In order to improve the performance of DOA estimation, neural network based methods [5]- [8] are introduced into the field of array signal processing. In [5], the problem of DOA estimation is modeled as classification task.…”

Section: Introductionmentioning

confidence: 99%

A Novel Phase Enhancement Method for Low-Angle Estimation Based on Supervised DNN Learning

et al. 2019

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In low-altitude target situation, the multi-path signals cause the amplitude-phase distortion of direct signal from targets and degrade the performance of existing methods. Hence, in this paper, we propose a phase enhancement method for low-angle estimation using supervised deep neural network (DNN) to mitigate the phase distortion, thus to improve direction of arrival (DOA) estimation accuracy. The mapping relationship between the original phase difference distribution of the received signal and desired phase difference distribution is learned by DNN during training. The phase of test data is enhanced by trained DNN, and the enhanced phase is used for DOA estimation. We explain the significance of enhancing phase instead of amplitude by discussing the sensitivity of amplitude and phase on DOA estimation. Moreover, we prove the effectiveness and superiority of the proposed method by simulation experiments. The results demonstrate that the proposed technique has a better performance in terms of estimation error and goodness of fit (GoF) than the physics-driven DOA estimation methods and state-of-the-art methods including feature reversal and the support vector regression (SVR). INDEX TERMS Phase enhancement, supervised deep neural network, DOA estimation.

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A Support Vector Regression-Based Method for Target Direction of Arrival Estimation From HF Radar Data

Cited by 42 publications

References 22 publications

A Review on Machine Learning-Based Radio Direction Finding

A Review on Machine Learning-Based Radio Direction Finding

Altitude measurement based on characteristics reversal by deep neural network for VHF radar

A Novel Phase Enhancement Method for Low-Angle Estimation Based on Supervised DNN Learning

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