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Optimization Methods for Solving the Low Autocorrelation Sidelobes Problem

Abstract : In this paper, a discussion is made on the optimization methods that can solve the low autocorrelation sidelobes problem for polyphase sequences. This paper starts with a description and a comparison of two algorithms that are commonly used in the literature: a stochastic method and a deterministic one (a gradient descent). Then, an alternative method based on the Random Walk Metropolis-Hastings algorithm is proposed, that takes the gradient as a search direction. It provides better results than a steepest descent alone. Finally, this autocorrelation question is handled differently, considering a mismatched filter. We will see that a mismatched filter performs impressively well on optimized sequences.
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https://hal.archives-ouvertes.fr/hal-01360260
Contributor : U. Tan <>
Submitted on : Friday, September 30, 2016 - 5:02:48 PM
Last modification on : Monday, August 24, 2020 - 4:16:05 PM
Long-term archiving on: : Sunday, January 1, 2017 - 12:13:29 PM

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U. Tan, O Rabaste, C Adnet, F Arlery, J.-P Ovarlez. Optimization Methods for Solving the Low Autocorrelation Sidelobes Problem. 2016 17th International Radar Symposium (IRS 2017), May 2016, Cracovie, Poland. pp.1-5, ⟨10.1109/IRS.2016.7497323⟩. ⟨hal-01360260⟩

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