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Communication Dans Un Congrès Année : 2016

Optimization Methods for Solving the Low Autocorrelation Sidelobes Problem

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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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hal-01360260 , version 1 (30-09-2016)

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U. Tan, O Rabaste, C Adnet, Fabien 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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