Published June 3, 2022
| Version v1
Publication
Topological phase estimation method for reparameterized periodic functions
Contributors
Others:
- Laboratoire Analyse et Mathématiques Appliquées (LAMA) ; Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel
- Understanding the Shape of Data (DATASHAPE) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Saclay - Ile de France ; Institut National de Recherche en Informatique et en Automatique (Inria)
- Laboratoire de Mathématiques Jean Leray (LMJL) ; Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST) ; Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)
- Nantes Université - École Centrale de Nantes (Nantes Univ - ECN) ; Nantes Université (Nantes Univ)
- Laboratoire de Mathématiques d'Orsay (LMO) ; Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
- ANR-19-CHIA-0001,TopAI,TopAI : Analyse Topologique des Données pour l'apprentissage et l'IA(2019)
Description
We consider a signal composed of several periods of a periodic function, of which we observe a noisy reparametrisation. The phase estimation problem consists of finding that reparametrisation, and, in particular, the number of observed periods. Existing methods are well-suited to the setting where the periodic function is known, or at least, simple. We consider the case when it is unknown and we propose an estimation method based on the shape of the signal. We use the persistent homology of sublevel sets of the signal to capture the temporal structure of its local extrema. We infer the number of periods in the signal by counting points in the persistence diagram and their multiplicities. Using the estimated number of periods, we construct an estimator of the reparametrisation. It is based on counting the number of sufficiently prominent local minima in the signal. This work is motivated by a vehicle positioning problem, on which we evaluated the proposed method.
Abstract
31 pages, 14 figuresAdditional details
Identifiers
- URL
- https://hal.archives-ouvertes.fr/hal-03687686
- URN
- urn:oai:HAL:hal-03687686v1
Origin repository
- Origin repository
- UNICA