Optimization Methods for Solving the Low Autocorrelation Sidelobes Problem
Résumé
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.
Mots clés
Optimization algorithms
Mismatched filter
Optimization methods
Polyphase sequences
Search direction
Steepest descent
Stochastic method
Convergence
Correlation
Cost function
Search problems
Signal to noise ratio
Aperiodic autocorrelation
Low autocorrelation sidelobes problem
Gradient descent
Random Walk Metropolis-Hastings algorithm
Gradient methods
Filtering theory
Correlation methods
| Origine | Fichiers produits par l'(les) auteur(s) |
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