UPM Institutional Repository

A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising


Citation

Al-Dabbagh, Mohanad Dawood and Al-Dabbagh, Rawaa Dawoud and Raja Abdullah, Raja Syamsul Azmir and Hashim, Fazirulhisyam (2015) A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising. Engineering Optimization, 47 (6). pp. 771-787. ISSN 0305-215X; ESSN: 1029-0273

Abstract

The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. As the standard DE algorithm is a fixed length optimizer, it is not suitable for solving signal de-noising problems that call for variability. A modified crossover scheme called rand-length crossover was designed to fit the proposed variable-length DE, and the new DE algorithm is referred to as the random variable-length crossover differential evolution (rvlx-DE) algorithm. The measurement results demonstrate a highly efficient capability for target detection in terms of frequency response and peak forming that was isolated from noise distortion. The modified method showed significant improvements in performance over traditional de-noising techniques.


Download File

[img]
Preview
PDF (Abstract)
A new modified differential evolution algorithm scheme.pdf

Download (4kB) | Preview

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1080/0305215X.2014.927449
Publisher: Taylor & Francis
Keywords: Chirp pulse generators; Differential evolution (DE); Target detection; Radar signal de-noising
Depositing User: Mohd Hafiz Che Mahasan
Date Deposited: 29 Jun 2016 01:11
Last Modified: 29 Sep 2016 04:40
Altmetrics: httphttp://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/0305215X.2014.927449
URI: http://psasir.upm.edu.my/id/eprint/43526
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item