Nonlinear Frequency-Modulated Waveforms Modeling and Optimization for Radar Applications

Zhihuo Xu, Xiaoyue Wang and Yuexia Wang ()
Additional contact information
Zhihuo Xu: Radar Signal Processing Group, School of Transportation, Nantong University, Nantong 226019, China
Xiaoyue Wang: Radar Signal Processing Group, School of Transportation, Nantong University, Nantong 226019, China
Yuexia Wang: Radar Signal Processing Group, School of Transportation, Nantong University, Nantong 226019, China

Mathematics, 2022, vol. 10, issue 21, 1-11

Abstract: Conventional radars commonly use a linear frequency-modulated (LFM) waveform as the transmitted signal. The LFM radar is a simple system, but its impulse-response function produces a −13.25 dB sidelobe, which in turn can make the detection of weak targets difficult by drowning out adjacent weak target information with the sidelobe of a strong target. To overcome this challenge, this paper presents a modeling and optimization method for non-linear frequency-modulated (NLFM) waveforms. Firstly, the time-frequency relationship model of the NLFM signal was combined by using the Legendre polynomial. Next, the signal was optimized by using a bio-inspired method, known as the Firefly algorithm. Finally, the numerical results show that the advantages of the proposed NLFM waveform include high resolution and high sensitivity, as well as ultra-low sidelobes without the loss of the signal-to-noise ratio (SNR). To the authors’ knowledge, this is the first study to use NLFM signals for target-velocity improvement measurements. Importantly, the results show that mitigating the sidelobe of the radar waveform can significantly improve the accuracy of the velocity measurements.

Keywords: modeling and optimization; non-linear frequency-modulated (NLFM) waveforms; Legendre polynomial; bio-inspired method; radar; signal processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/21/3939/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/21/3939/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:21:p:3939-:d:951345

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().