An IMM Filter Defined in the Linear-Circular Domain, Application to Maneuver Detection with Heading Only. (6th November 2018)

• Record Type: Journal Article
• Authors: El mokhtari, Karim; Reboul, Serge; Stienne, Georges; Choquel, Jean Bernard; Amami, Benaissa; Benjelloun, Mohammed
• Other Names: Eftekhar Azam Saeed Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2018(2018)
• Publication Date: 2018-11-06
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2018/3531075
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : In this article, we propose a multimodel filter for circular data. The so-called Circular Interacting Multimodel filter is derived in a Bayesian framework with the circular normal von Mises distribution. The aim of the proposed filter is to obtain the same performance in the circular domain as the classical IMM filter in the linear domain. In our approach, the mixing and fusion stages of the Circular Interacting Multimodel filter are, respectively, defined from the a priori and from the a posteriori circular distributions of the state angle knowing the measurements and according to a set of models. We propose in this article a set of circular models that will be used in order to detect the vehicle maneuvers from heading measurements. The Circular Interacting Multimodel filter performances are assessed on synthetic data and we show on real data a vehicle maneuver detection application.