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Optimization Methods for Solving the Low Autocorrelation Sidelobes Problem

Tan, Uly Hour and Rabaste, Olivier and Adnet, Claude and Arlery, Fabien and Ovarlez, Jean-Philippe Optimization Methods for Solving the Low Autocorrelation Sidelobes Problem. (2016) In: 2016 17th International Radar Symposium (IRS), 10 May 2016 - 12 May 2016 (Krakow, Poland).

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Official URL: http://doi.org/10.1109/IRS.2016.7497323


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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to the IEEE (Institute of Electrical and Electronics Engineers). This paper is available at : https://ieeexplore.ieee.org/abstract/document/7497323 “© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
HAL Id:hal-02087554
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE)
Other partners > Thales (FRANCE)
Other partners > CentraleSupélec (FRANCE)
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Deposited On:01 Apr 2019 15:16

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