Ultrawideband Direction-of-Arrival Estimation Using Complex-Valued Spatiotemporal Neural Networks

Terabayashi, Kenji; Natsuaki, Ryo; Hirose, Akira

doi:10.1109/tnnls.2014.2313869

Cited by 38 publications

(11 citation statements)

References 24 publications

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“…Terabayashi et al [20] proposed a DOA estimation method using a complex-valued spatiotemporal NN (CVSTNN) for ultrawideband (UWB) systems. e CVSTNN was combined with the power-inversion adaptive array (PIAA) for null steering without the knowledge of incident directions.…”

Section: Complex Signalsmentioning

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: Complex Signalsmentioning

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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“…The DOA is estimated by the reconstructed array output using a refined 1-D searching procedure. In recent decades, a promising technique called machine learning has been widely used in the DOA estimation problem as well [ 19 , 20 , 21 ]. These methods establish training sets with a DOA label first, and then derive a mapping from the array outputs to the DOA with existing machine-learning techniques.…”

Section: Introductionmentioning

confidence: 99%

A High-Precision Algorithm for DOA Estimation Using a Long-Baseline Array Based on the Hearing Mechanism of the Ormia Ochracea

Huang

Feng

2022

Sensors

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Inspired by the Ormia Ochracea hearing mechanism, a new direction of arrival estimation using multiple antenna arrays has been considered in spatially colored noise fields. This parasitoid insect can locate s cricket’s position accurately using the small distance between its ears, far beyond the standard array with the same aperture. This phenomenon can be understood as a mechanical coupled structure existing between the Ormia ears. The amplitude and phase differences between the received signals are amplified by the mechanical coupling, which is functionally equivalent to a longer baseline. In this paper, we regard this coupled structure as a multi-input multi-output filter, where coupling exists between each pair of array elements. Then, an iterative direction-finding algorithm based on fourth-order cumulants with fully coupled array is presented. In this manner, the orientation of the mainlobe can direct at the incident angle. Hence, the direction-finding accuracy can be improved in all possible incident angles. We derive the Cramér-Rao lower bound for our proposed algorithm and validate its performance based on simulations. Our proposed DOA estimation algorithm is superior to the existing biologically inspired direction-finding and fourth-order cumulants-based estimation algorithms.

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“…Conventional DOA estimation methods, including beamforming techniques [1][2][3] and subspace-based methods [4][5][6][7][8], are primarily used in point source scenarios. With the development of machine learning and artificial intelligence, neural network (NN) has been applied in the DOA estimation domain [9][10][11][12][13][14]. This method establishes training datasets with DOA labels first, and then derives a mapping from antenna outputs to signal directions with existing methods.…”

Section: Introductionmentioning

confidence: 99%

Direction-of-Arrival Estimation over Sea Surface from Radar Scattering Based on Convolutional Neural Network

2021

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Conventional direction-of-arrival (DOA) estimation methods are primarily used in point source scenarios and based on array signal processing. However, due to the local scattering caused by sea surface, signals observed from radar antenna cannot be regarded as a point source but rather as a spatially dispersed source. Besides, with the advantages of flexibility and comparably low cost, synthetic aperture radar (SAR) is the present and future trend of space-based systems. This paper proposes a novel DOA estimation approach for SAR systems using the simulated radar measurement of the sea surface at different operating frequencies and wind speeds. This article’s forward model is an advanced integral equation model (AIEM) to calculate the electromagnetic scattered from the sea surface. To solve the DOA estimation problem, we introduce a convolutional neural network (CNN) framework to estimate the transmitter’s incident angle and incident azimuth angle. Results demonstrate that the CNN can achieve a good performance in DOA estimation at a wide range of frequencies and sea wind speeds.

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Ultrawideband Direction-of-Arrival Estimation Using Complex-Valued Spatiotemporal Neural Networks

Cited by 38 publications

References 24 publications

A Review on Machine Learning-Based Radio Direction Finding

A Review on Machine Learning-Based Radio Direction Finding

A High-Precision Algorithm for DOA Estimation Using a Long-Baseline Array Based on the Hearing Mechanism of the Ormia Ochracea

Direction-of-Arrival Estimation over Sea Surface from Radar Scattering Based on Convolutional Neural Network

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