High-Resolution Waveform Design and Optimal Target Detection for Compressive Sensing Radar Using Genetic Algorithms

Hizbullah Qureshi; Atif Mahmood; Ayman Muhammad Abdallah

doi:10.1109/ICECCE67514.2025.11257898

High-Resolution Waveform Design and Optimal Target Detection for Compressive Sensing Radar Using Genetic Algorithms

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Waveform design is pivotal in enhancing signal reconstruction and target detection in Compressive Sensing Radar (CSR). This paper proposes a Genetic Algorithm (GA)-based waveform optimization framework that minimizes the Exact Recovery Condition (ERC) while satisfying constant-envelope transmission constraints. Unlike conventional waveforms, the proposed design directly targets sparse recovery criteria, improving target resolvability in cluttered scenes. Simulation results demonstrate enhanced detection accuracy and ambiguity suppression, outperforming Alltop, LFM, and Barker sequences in both ambiguity function shape and ERC behavior.

Original language	English
Title of host publication	6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331549152
DOIs	https://doi.org/10.1109/ICECCE67514.2025.11257898
State	Published - 2025
Event	6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Istanbul, Turkey Duration: 27 Aug 2025 → 28 Aug 2025

Publication series

Name	6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings

Conference

Conference	6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025
Country/Territory	Turkey
City	Istanbul
Period	27/08/25 → 28/08/25

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

Compressive Sensing (CS)
Compressive Sensing Radar (CSR)
Exact Recovery Condition (ERC)
Genetic Algorithm (GA)

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Control and Optimization
Computer Networks and Communications
Computer Science Applications

Access to Document

10.1109/ICECCE67514.2025.11257898

Cite this

Qureshi, H., Mahmood, A., & Abdallah, A. M. (2025). High-Resolution Waveform Design and Optimal Target Detection for Compressive Sensing Radar Using Genetic Algorithms. In 6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings (6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECCE67514.2025.11257898

Qureshi, Hizbullah ; Mahmood, Atif ; Abdallah, Ayman Muhammad. / High-Resolution Waveform Design and Optimal Target Detection for Compressive Sensing Radar Using Genetic Algorithms. 6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025. (6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings).

@inproceedings{8c98968d06884edcb816d20f066de948,

title = "High-Resolution Waveform Design and Optimal Target Detection for Compressive Sensing Radar Using Genetic Algorithms",

abstract = "Waveform design is pivotal in enhancing signal reconstruction and target detection in Compressive Sensing Radar (CSR). This paper proposes a Genetic Algorithm (GA)-based waveform optimization framework that minimizes the Exact Recovery Condition (ERC) while satisfying constant-envelope transmission constraints. Unlike conventional waveforms, the proposed design directly targets sparse recovery criteria, improving target resolvability in cluttered scenes. Simulation results demonstrate enhanced detection accuracy and ambiguity suppression, outperforming Alltop, LFM, and Barker sequences in both ambiguity function shape and ERC behavior.",

keywords = "Compressive Sensing (CS), Compressive Sensing Radar (CSR), Exact Recovery Condition (ERC), Genetic Algorithm (GA)",

author = "Hizbullah Qureshi and Atif Mahmood and Abdallah, \{Ayman Muhammad\}",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 ; Conference date: 27-08-2025 Through 28-08-2025",

year = "2025",

doi = "10.1109/ICECCE67514.2025.11257898",

language = "English",

series = "6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings",

address = "United States",

}

Qureshi, H, Mahmood, A & Abdallah, AM 2025, High-Resolution Waveform Design and Optimal Target Detection for Compressive Sensing Radar Using Genetic Algorithms. in 6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings. 6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025, Istanbul, Turkey, 27/08/25. https://doi.org/10.1109/ICECCE67514.2025.11257898

High-Resolution Waveform Design and Optimal Target Detection for Compressive Sensing Radar Using Genetic Algorithms. / Qureshi, Hizbullah; Mahmood, Atif ; Abdallah, Ayman Muhammad.
6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025. (6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - High-Resolution Waveform Design and Optimal Target Detection for Compressive Sensing Radar Using Genetic Algorithms

AU - Qureshi, Hizbullah

AU - Mahmood, Atif

AU - Abdallah, Ayman Muhammad

PY - 2025

Y1 - 2025

N2 - Waveform design is pivotal in enhancing signal reconstruction and target detection in Compressive Sensing Radar (CSR). This paper proposes a Genetic Algorithm (GA)-based waveform optimization framework that minimizes the Exact Recovery Condition (ERC) while satisfying constant-envelope transmission constraints. Unlike conventional waveforms, the proposed design directly targets sparse recovery criteria, improving target resolvability in cluttered scenes. Simulation results demonstrate enhanced detection accuracy and ambiguity suppression, outperforming Alltop, LFM, and Barker sequences in both ambiguity function shape and ERC behavior.

AB - Waveform design is pivotal in enhancing signal reconstruction and target detection in Compressive Sensing Radar (CSR). This paper proposes a Genetic Algorithm (GA)-based waveform optimization framework that minimizes the Exact Recovery Condition (ERC) while satisfying constant-envelope transmission constraints. Unlike conventional waveforms, the proposed design directly targets sparse recovery criteria, improving target resolvability in cluttered scenes. Simulation results demonstrate enhanced detection accuracy and ambiguity suppression, outperforming Alltop, LFM, and Barker sequences in both ambiguity function shape and ERC behavior.

KW - Compressive Sensing (CS)

KW - Compressive Sensing Radar (CSR)

KW - Exact Recovery Condition (ERC)

KW - Genetic Algorithm (GA)

UR - https://www.scopus.com/pages/publications/105030102130

U2 - 10.1109/ICECCE67514.2025.11257898

DO - 10.1109/ICECCE67514.2025.11257898

M3 - Conference contribution

AN - SCOPUS:105030102130

T3 - 6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings

BT - 6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025

Y2 - 27 August 2025 through 28 August 2025

ER -

Qureshi H, Mahmood A , Abdallah AM. High-Resolution Waveform Design and Optimal Target Detection for Compressive Sensing Radar Using Genetic Algorithms. In 6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2025. (6th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2025 - Conference Proceedings). doi: 10.1109/ICECCE67514.2025.11257898

High-Resolution Waveform Design and Optimal Target Detection for Compressive Sensing Radar Using Genetic Algorithms

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this