A Novel Method of Deinterleaving Pulse Repetition Interval Modulated Sparse Sequences in Noisy Environments
Summary :
This letter presents a novel method based on sparsity, to solve the problem of deinterleaving pulse trains. The proposed method models the problem of deinterleaving pulse trains as an underdetermined system of linear equations. After determining the mixing matrix, we find sparsest solution of an underdetermined system of linear equations using basis pursuit denoising. This method is superior to previous ones in a number of aspects. First, spurious and missing pulses would not cause any performance reduction in the algorithm. Second, the algorithm works well despite the type of pulse repetition interval modulation that is used. Third, the proposed method is able to separate similar sources.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E97-A No.5 pp.1136-1139
- Publication Date
- 2014/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E97.A.1136
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Mahmoud KESHAVARZI
Sharif University of Technology
Delaram AMIRI
Shiraz University
Amir Mansour PEZESHK
Sharif University of Technology
Forouhar FARZANEH
Sharif University of Technology
Keyword
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Mahmoud KESHAVARZI, Delaram AMIRI, Amir Mansour PEZESHK, Forouhar FARZANEH, "A Novel Method of Deinterleaving Pulse Repetition Interval Modulated Sparse Sequences in Noisy Environments" in IEICE TRANSACTIONS on Fundamentals,
vol. E97-A, no. 5, pp. 1136-1139, May 2014, doi: 10.1587/transfun.E97.A.1136.
Abstract: This letter presents a novel method based on sparsity, to solve the problem of deinterleaving pulse trains. The proposed method models the problem of deinterleaving pulse trains as an underdetermined system of linear equations. After determining the mixing matrix, we find sparsest solution of an underdetermined system of linear equations using basis pursuit denoising. This method is superior to previous ones in a number of aspects. First, spurious and missing pulses would not cause any performance reduction in the algorithm. Second, the algorithm works well despite the type of pulse repetition interval modulation that is used. Third, the proposed method is able to separate similar sources.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E97.A.1136/_p
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@ARTICLE{e97-a_5_1136,
author={Mahmoud KESHAVARZI, Delaram AMIRI, Amir Mansour PEZESHK, Forouhar FARZANEH, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Novel Method of Deinterleaving Pulse Repetition Interval Modulated Sparse Sequences in Noisy Environments},
year={2014},
volume={E97-A},
number={5},
pages={1136-1139},
abstract={This letter presents a novel method based on sparsity, to solve the problem of deinterleaving pulse trains. The proposed method models the problem of deinterleaving pulse trains as an underdetermined system of linear equations. After determining the mixing matrix, we find sparsest solution of an underdetermined system of linear equations using basis pursuit denoising. This method is superior to previous ones in a number of aspects. First, spurious and missing pulses would not cause any performance reduction in the algorithm. Second, the algorithm works well despite the type of pulse repetition interval modulation that is used. Third, the proposed method is able to separate similar sources.},
keywords={},
doi={10.1587/transfun.E97.A.1136},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - A Novel Method of Deinterleaving Pulse Repetition Interval Modulated Sparse Sequences in Noisy Environments
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1136
EP - 1139
AU - Mahmoud KESHAVARZI
AU - Delaram AMIRI
AU - Amir Mansour PEZESHK
AU - Forouhar FARZANEH
PY - 2014
DO - 10.1587/transfun.E97.A.1136
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E97-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2014
AB - This letter presents a novel method based on sparsity, to solve the problem of deinterleaving pulse trains. The proposed method models the problem of deinterleaving pulse trains as an underdetermined system of linear equations. After determining the mixing matrix, we find sparsest solution of an underdetermined system of linear equations using basis pursuit denoising. This method is superior to previous ones in a number of aspects. First, spurious and missing pulses would not cause any performance reduction in the algorithm. Second, the algorithm works well despite the type of pulse repetition interval modulation that is used. Third, the proposed method is able to separate similar sources.
ER -
English
