[Online]A Genetic Algorithm-Based OFDM Waveform Design for Low-Angle Target Tracking

A Genetic Algorithm-Based OFDM Waveform Design for Low-Angle Target Tracking
ID:133 Submission ID:115 View Protection:ATTENDEE Updated Time:2025-12-23 13:12:32 Hits:298 Online

Start Time:2025-12-29 15:00 (Asia/Amman)

Duration:15min

Session:[S4] Track 4: Dedicated Technologies for Wireless Networks Track 6: Signal Processing for Wireless Communications Track 8: Communication and Networking Technologies for Smart Agriculture » [S4] Track 4: Dedicated Technologies for Wireless NetworksTrack 6: Signal Processing for Wireless CommunicationsTrack 8: Communication and Networking Technologies for Smart Agriculture

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Abstract
Accurate detection and tracking of low-altitude targets, such as drones, is a cornerstone of security in smart cities but is profoundly challenged by multipath propagation and atmospheric distortions in low-angle scenarios. This paper introduces a novel radar waveform design to overcome these limitations. Our method focuses on optimizing an Orthogonal Frequency Division Multiplexing (OFDM) waveform by shaping its Wideband Ambiguity Function (WAF) to approach an ideal, high-resolution profile. The optimization is driven by a Genetic Algorithm (GA) that efficiently navigates the complex parameter space to minimize a defined cost function. Simulation results demonstrate that our designed waveform achieves a significant performance leap over existing techniques. Key improvements include a 22 dB suppression of the first ambiguity sidelobe and a dramatic reduction in tracking error. Quantitative analysis shows our method achieves a 41.66% greater improvement in RMSE versus SNR and a 66.66% greater improvement in RMSE versus Range compared to a leading benchmark. This work establishes that GA-optimized OFDM waveforms are a powerful tool for enhancing radar resolution and tracking precision, directly addressing critical safety needs in modern urban surveillance systems.
Keywords
low-angle scenarios, OFDM signal, wideband ambiguity function, Genetic Algorithm, multipath effects.
Speaker
Zohreh Asadsangabi
Researcher Shiraz University of Technology

Submission Author
Zohreh Asadsangabi Shiraz University of Technology
Reza Mohseni Shiraz University of Technology
Hattar Hattar Zarqa University
Mohamed Hafez INTI-IU-University;Shinawatra University
Mohamadreza Khosravi Shiraz University of Medical Sciences
Mahdi Jafari Shiraz University
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