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Nonlinear Frequency-Modulated Waveforms Modeling and Optimization for Radar Applications

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhihuo Xu & Xiaoyue Wang & Yuexia Wang, 2022. "Nonlinear Frequency-Modulated Waveforms Modeling and Optimization for Radar Applications," Mathematics, MDPI, vol. 10(21), pages 1-11, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3939-:d:951345
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