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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Radar Signal Recognition Using the Continuous Wavelet Transform and Artificial Neural Network |
| Authors Name: | LOPAMUDRA JENA |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_187088
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| Published Paper Id: | IJRTI2306018 |
| Published In: | Volume 8 Issue 6, June-2023 |
| DOI: | |
| Abstract: | Radar signal recognition algorithms have proven to be highly valuable in various fields such as intelligent radio, surveillance systems, and electronic warfare. The ability to quickly and accurately identify different signals and their emitters is crucial in the rapidly changing electromagnetic environment. The use of CWT is an effective method for extracting signal and modulation features for classification purposes. The paper focuses on the ability of various types of radar signals to be recognized, such as linear frequency modulated signals (LFM) and stepped frequency modulated signals (SFM), phase-coded waveforms (PCW) signals with Barker code, and rectangular pulses (Rec). The algorithm proposes using higher-order statistics (HOS) of CWT coefficients as signal features. PCA is used to reduce the dimensionality of the feature space. PCA identifies and reduces the dimensions of the most important features while retaining as much relevant information as possible. Finally, as the classifier, a feed-forward neural network is used to classify the signals based on the extracted features. The paper explores the effectiveness of using CWT and its coefficients for signal recognition. The HOS-based approach with PCA and a feed-forward neural network offers a more traditional feature extraction and classification pipeline. Overall, algorithms provide viable methods for signal recognition, and their suitability may depend on factors such as the nature of the signals, available training data, and computational requirements. |
| Keywords: | radar signal recognition; continuous wavelet transform (CWT); feature extraction; artificial neural network (ANN). |
| Cite Article: | "Radar Signal Recognition Using the Continuous Wavelet Transform and Artificial Neural Network", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 6, page no.96 - 99, June-2023, Available :http://www.ijrti.org/papers/IJRTI2306018.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2306018
Registration ID:187088
Published In: Volume 8 Issue 6, June-2023
DOI (Digital Object Identifier):
Page No: 96 - 99 Country: Bhubaneswar, Odisha, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2306018 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2306018 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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