Suppressing Autocorrelation Sidelobes of LFM Pulse Trains with Genetic Algorithm

Peng Wang, Huadong Meng, Xiqin Wang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Modulations and diversities, including the Costas-ordered stepped-frequency and nonlinear stepped-frequency waveforms are widely used in linear frequency modulation (LFM) pulse trains to reduce the relatively high autocorrelation function (ACF) sidelobes. An efficient method was developed to optimize the interpulse frequency modulation to remove most of the ACF sidelobes about the mainlobe peak, with only a small increase in the mainlobe width. The genetic algorithm is used to solve the nonlinear optimization problem to find the interpulse frequency modulation sequence. The effects on the ACF sidelobes suppression and mainlobe widening are studied. The results show that the new design is superior to the corresponding stepped-frequency LFM signal and weighted stepped-frequency LFM signal in the terms of the ACF sidelobes reduction and mainlobe spread.

Original languageEnglish
Pages (from-to)800-806
Number of pages7
JournalTsinghua Science and Technology
Volume13
Issue number6
DOIs
StatePublished - Dec 2008
Externally publishedYes

Keywords

  • autocorrelation function (ACF)
  • coherent train
  • genetic algorithm (GA)
  • performance improvement
  • sidelobes suppression

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