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A New Scattering Clusters Equalization Algorithm for Airtarget Acoustic Passive Detection and Classification

by Mazhar Tayel, Mahmoud Sabry
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 170 - Number 1
Year of Publication: 2017
Authors: Mazhar Tayel, Mahmoud Sabry
10.5120/ijca2017914665

Mazhar Tayel, Mahmoud Sabry . A New Scattering Clusters Equalization Algorithm for Airtarget Acoustic Passive Detection and Classification. International Journal of Computer Applications. 170, 1 ( Jul 2017), 29-34. DOI=10.5120/ijca2017914665

@article{ 10.5120/ijca2017914665,
author = { Mazhar Tayel, Mahmoud Sabry },
title = { A New Scattering Clusters Equalization Algorithm for Airtarget Acoustic Passive Detection and Classification },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2017 },
volume = { 170 },
number = { 1 },
month = { Jul },
year = { 2017 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume170/number1/28036-2017914665/ },
doi = { 10.5120/ijca2017914665 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:17:20.029363+05:30
%A Mazhar Tayel
%A Mahmoud Sabry
%T A New Scattering Clusters Equalization Algorithm for Airtarget Acoustic Passive Detection and Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 170
%N 1
%P 29-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Capture of airtarget acoustic signal carries a lot of information that can be used for its characterization. The airtarget acoustic signal can be used as a passive detection and classification technique. In this paper, a proposed flexible algorithm for airtarget type passive detection and classification is introduced to extract some selected unique features to classify airtargets. Also, a proposed equalization method is introduced to characterize the airtargets according to their extracted features.

References
  1. Mazhar Tayel and Mahmoud Sabry. 2016. A Detection and Identification Method for Airtarget Acoustic Signal Characterization. IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 6, Issue 2, Ver. I (Mar. -Apr. 2016), PP 21-24 e-ISSN: 2319 – 4200, p-ISSN No. : 2319 – 4197.
  2. Mazhar Tayel and Mahmoud Sabry. 2016. A Proposed Hybrid Method for Airtarget Acoustic Signature Diagnosis. International Journal of Science and Research (IJSR) 2319-7064.
  3. Anamitra Bardhan Roy. 2012. Comparison of FFT, DCT, DWT, WHT Compression Techniques on Electrocardiogram. Special Issue of International Journal of Computer Applications 0975 – 8887.
  4. Erik Axel Rønnevig Nielsen and DR. Anne Cathrine Elster 2007. Real-Time Wavelet Filtering on the GPU. Norwegian University of Science and Technology Thesis. May 2007.
  5. Singh RP, Dixit M. 2015. Histogram Equalization Techniques for Image Enhancement .International Journal of Signal Processing, Image Processing and Pattern Recognition 345-52.
  6. Airplane Sounds, Jet Sounds. Available. 2017. http://www.partnersinrhyme.com/soundfx/airplanesoundfx.shtml.
  7. Airplane Sounds, Jet Sounds. Available. 2017. http://www.aviationtrivia.info/AIRCRAFT-WAV-SOUNDS.php.
Index Terms

Computer Science
Information Sciences

Keywords

Airtarget Acoustic signal Short time series Decomposition domain and Equalization analysis.

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