ORIGINAL ARTICLE

An efficient direction-of-arrival estimation of multipath signals with impulsive noise using satin bowerbird optimization-based deep learning neural network

Corresponding Author

Harikrushna Gantayat

orcid.org/0000-0002-4980-278X

Department of Electronics and Communication Engineering, National Institute of Science and Technology, Berhampur, Biju Patnaik University of Technology, Rourkela, India

Department of Electronics and Communication Engineering, Biju Patnaik University of Technology, Rourkela, India

Correspondence

Harikrushna Gantayat, Department of Electronics and Communication Engineering, National Institute of Science and Technology, Biju Patnaik University of Technology, Rourkela, India.

Email: [email protected] and [email protected]

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Trilochan Panigrahi,

Trilochan Panigrahi

Department of Electronics and Communication Engineering, National Institute of Technology, Ponda, Goa, India

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Pradyumna Patra,

Pradyumna Patra

Department of Electronics and Communication Engineering, National Institute of Science and Technology, Berhampur, Biju Patnaik University of Technology, Rourkela, India

Department of Electronics and Communication Engineering, Biju Patnaik University of Technology, Rourkela, India

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Harikrushna Gantayat,

Corresponding Author

Harikrushna Gantayat

orcid.org/0000-0002-4980-278X

Department of Electronics and Communication Engineering, National Institute of Science and Technology, Berhampur, Biju Patnaik University of Technology, Rourkela, India

Department of Electronics and Communication Engineering, Biju Patnaik University of Technology, Rourkela, India

Correspondence

Harikrushna Gantayat, Department of Electronics and Communication Engineering, National Institute of Science and Technology, Biju Patnaik University of Technology, Rourkela, India.

Email: [email protected] and [email protected]

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Trilochan Panigrahi,

Trilochan Panigrahi

Department of Electronics and Communication Engineering, National Institute of Technology, Ponda, Goa, India

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Pradyumna Patra,

Pradyumna Patra

Department of Electronics and Communication Engineering, National Institute of Science and Technology, Berhampur, Biju Patnaik University of Technology, Rourkela, India

Department of Electronics and Communication Engineering, Biju Patnaik University of Technology, Rourkela, India

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First published: 25 July 2022

https://doi.org/10.1111/exsy.13108

Citations: 1

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Abstract

There exist numerous multi-path signals with impulsive noise (IN) in a multi-path propagation environment. The direction-of-arrival (DOA) is highly challenging to be assessed because of the strong IN. For various military along with civilian applications, DOA estimation has turned into a hopeful technology. But, estimating DOA for multipath signals with IN environments is extremely complicated. A desirable output is not attained by several existent techniques, namely subspace-centric approaches, maximum likelihood techniques, along with sparse representation-centric methods, for DOA assessment. Therefore, an effective satin bowerbird optimization-based deep learning neural network centered DOA estimation upon single snapshots is proposed in this paper for attaining robust and precise DOA estimation in multipath and IN environments. An arbitrary array configuration is employed by utilizing the proposed work as an input signal for establishing the received signal's model. Pre-processing, time-frequency domain conversion, feature extraction, feature reduction and DOA estimation are the proposed method's 5 disparate stages. To evince the proposed model's efficacy, its outcomes are analogized with other prevailing techniques regarding some performance metrics. The proposed model attains the root mean square error of 0.6484366-AZ and 0.6484366-EA, root mean squared percentage error (RMPSE) of 4.636107-AZ and 5.628599-EA and probability of resolution of 0.967999-AZ and 0.962841-EA, which are extremely less when compared to the prevailing methods; thus, depicting the superiority of the proposed model for an effective DOA estimation.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

Open Research

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Volume41, Issue6

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June 2024

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An efficient direction-of-arrival estimation of multipath signals with impulsive noise using satin bowerbird optimization-based deep learning neural network

Abstract

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An efficient direction-of-arrival estimation of multipath signals with impulsive noise using satin bowerbird optimization-based deep learning neural network

Abstract

CONFLICT OF INTEREST

Open Research

DATA AVAILABILITY STATEMENT

REFERENCES

Citing Literature

References

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