Angle of arrival estimator based on artificial neural networks

Efimov, Evgeny; Shevgunov, Timofey; Filimonova, Daria

doi:10.1109/irs.2016.7497355

Cited by 10 publications

(4 citation statements)

References 2 publications

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“…Several works [134,135,136,137,138,139] address the problem of DoA estimation in array signal processing using artificial neural networks (ANNs). Let us look at each of these solutions.…”

Section: Adaptive Array Processingmentioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

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The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network's optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning's popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms.This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

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“…Several works [134,135,136,137,138,139] address the problem of DoA estimation in array signal processing using artificial neural networks (ANNs). Let us look at each of these solutions.…”

Section: Adaptive Array Processingmentioning

confidence: 99%

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

et al. 2019

View full text Add to dashboard Cite

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“…Efimov et al [62] presented the approach to the design of AOA estimator for narrowband noise-like signal based on NN to improve the signal processing speed. e signal time delay of each sensor pair was used as the model input and the associated AOA was designed as the model output.…”

Section: Capability Enhancementmentioning

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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“…Second-order cyclostationarity, or wide-sense cyclostationarity, appears to be the intrinsic property exhibited by processes generating signals belonging to different classes. Thus, there are plenty of examples where it was reasonably taken into account in many research fields and engineering applications, including communication signals with various modulation schemes [ 6 ], radio signals used in passive radars [ 7 , 8 ], electromagnetic measurements with near-field probes [ 9 , 10 ], spectral sensing in cognitive radio [ 11 ], mechanical vibration of rotary machines [ 12 , 13 ], radio astronomy [ 14 , 15 ], power analysis of electric circuits [ 16 ], generalized detection [ 17 , 18 ] under conditions of the least certainty, electromagnetic compatibility [ 19 , 20 ], detection of vital signs in radar response [ 21 , 22 ], drone navigation signals [ 23 ] and others.…”

Section: Introductionmentioning

confidence: 99%

Estimation of a Spectral Correlation Function Using a Time-Smoothing Cyclic Periodogram and FFT Interpolation—2N-FFT Algorithm

Shevgunov

Efimov

Guschina

2022

Sensors

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This article addresses the problem of estimating the spectral correlation function (SCF), which provides quantitative characterization in the frequency domain of wide-sense cyclostationary properties of random processes which are considered to be the theoretical models of observed time series or discrete-time signals. The theoretical framework behind the SCF estimation is briefly reviewed so that an important difference between the width of the resolution cell in bifrequency plane and the step between the centers of neighboring cells is highlighted. The outline of the proposed double-number fast Fourier transform algorithm (2N-FFT) is described in the paper as a sequence of steps directly leading to a digital signal processing technique. The 2N-FFT algorithm is derived from the time-smoothing approach to cyclic periodogram estimation where the spectral interpolation based on doubling the FFT base is employed. This guarantees that no cyclic frequency is left out of the coverage grid so that at least one resolution element intersects it. A numerical simulation involving two processes, a harmonic amplitude modulated by stationary noise and a binary-pulse amplitude-modulated train, demonstrated that their cyclic frequencies are estimated with a high accuracy, reaching the size of step between resolution cells. In addition, the SCF components estimated by the proposed algorithm are shown to be similar to the curves provided by the theoretical models of the observed processes. The comparison between the proposed algorithm and the well-known FFT accumulation method in terms of computational complexity and required memory size reveals the cases where the 2N-FFT algorithm offers a reasonable trade-off.

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Angle of arrival estimator based on artificial neural networks

Cited by 10 publications

References 2 publications

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

A Review on Machine Learning-Based Radio Direction Finding

Estimation of a Spectral Correlation Function Using a Time-Smoothing Cyclic Periodogram and FFT Interpolation—2N-FFT Algorithm

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