Direction of arrival estimation using Lévy flight-based moth flame optimization algorithm
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Abstract
In the field of 4G/5G communication, an important area of research is estimating the direction of incoming signals. The direction of narrow band sources can be determined using different spectral and eigenstructure techniques. When the signal-to-noise ratio (SNR) remains minimal and the channel is coherent, these methods fail to predict signal direction. Maximum likelihood (ML) is a statistical direction of estimation technique that overcomes the limitations of conventional algorithm and precisely discoveries signals in adverse conditions. ML approximation is estimated by minimalizing the complex log-likelihood function through indeterminable parameters. In this chapter, author proposed the modified Lévy flight mechanism- based moth flame optimization algorithm (LVMFO) to estimate the signal direction in low SNR environment. Moth flame optimization is a swarm intelligence algorithm that has good exploitation capability but has poor exploration capability; therefore, Lévy flight mechanism is incorporated in MFO to improve the exploration capability. The proposed improved LVMFO algorithm outperforms CAPON, MUSIC, and sine-cosine algorithm in terms of root mean square error and probability of resolution.
Abstract
In the field of 4G/5G communication, an important area of research is estimating the direction of incoming signals. The direction of narrow band sources can be determined using different spectral and eigenstructure techniques. When the signal-to-noise ratio (SNR) remains minimal and the channel is coherent, these methods fail to predict signal direction. Maximum likelihood (ML) is a statistical direction of estimation technique that overcomes the limitations of conventional algorithm and precisely discoveries signals in adverse conditions. ML approximation is estimated by minimalizing the complex log-likelihood function through indeterminable parameters. In this chapter, author proposed the modified Lévy flight mechanism- based moth flame optimization algorithm (LVMFO) to estimate the signal direction in low SNR environment. Moth flame optimization is a swarm intelligence algorithm that has good exploitation capability but has poor exploration capability; therefore, Lévy flight mechanism is incorporated in MFO to improve the exploration capability. The proposed improved LVMFO algorithm outperforms CAPON, MUSIC, and sine-cosine algorithm in terms of root mean square error and probability of resolution.
Chapters in this book
- Frontmatter I
- Acknowledgments V
- Preface VII
- Contents XI
- Editors’ biographies XIII
- Long short-term memory (LSTM) deep neural networks for sentiment classification 1
- Plant disease identification using IoT and deep learning algorithms 11
- A comprehensive study of plant pest and disease detection using different computer vision techniques 47
- Artificial intelligence applied to multiand broadband antenna design 69
- Direction of arrival estimation using Lévy flight-based moth flame optimization algorithm 107
- NLP techniques, tools, and algorithms for data science 123
- Prediction of coronary artery disease using logistic regression 149
- Design of antenna with biocomputing approach 159
- Energy-efficient methods for railway monitoring using WSN 179
- Analysis of acoustic emission for milling operation using artificial neural networks 203
- Index 221
Chapters in this book
- Frontmatter I
- Acknowledgments V
- Preface VII
- Contents XI
- Editors’ biographies XIII
- Long short-term memory (LSTM) deep neural networks for sentiment classification 1
- Plant disease identification using IoT and deep learning algorithms 11
- A comprehensive study of plant pest and disease detection using different computer vision techniques 47
- Artificial intelligence applied to multiand broadband antenna design 69
- Direction of arrival estimation using Lévy flight-based moth flame optimization algorithm 107
- NLP techniques, tools, and algorithms for data science 123
- Prediction of coronary artery disease using logistic regression 149
- Design of antenna with biocomputing approach 159
- Energy-efficient methods for railway monitoring using WSN 179
- Analysis of acoustic emission for milling operation using artificial neural networks 203
- Index 221
