This paper deals with motion modeling for 2-dimensional tracking of a maneuvering object. Specifically, a new class of nonlinear dynamic motion models, called Lambda:Omicron, is introduced with the purpose of accurately modeling maneuvers (regarded as variations of speed and turning rate) of the moving object. These models rely on the unicycle navigation model, suitably augmented with two chains of integrators to account for the unknown speed and turning rate command inputs. Quasi-exact time-discretization of the continuous-time Lambda:Omicron models is also carried out to allow their exploitation in nonlinear recursive filters. Simulation experiments are presented to show the effectiveness of the proposed models as compared to state-of-the-art linear and nonlinear motion models for tracking of strongly maneuvering objects.

Lambda:Omicron - A new prediction model to track maneuvering objects / Matteo Tesori; Giorgio Battistelli; Luigi Chisci; Alfonso Farina; Graziano Alfredo Manduzio. - ELETTRONICO. - (2022), pp. 1-8. (Intervento presentato al convegno 25th International Conference on Information Fusion tenutosi a Linköping nel 04-07 July 2022) [10.23919/FUSION49751.2022.9841370].

Lambda:Omicron - A new prediction model to track maneuvering objects

Matteo Tesori;Giorgio Battistelli;Luigi Chisci;Graziano Alfredo Manduzio
2022

Abstract

This paper deals with motion modeling for 2-dimensional tracking of a maneuvering object. Specifically, a new class of nonlinear dynamic motion models, called Lambda:Omicron, is introduced with the purpose of accurately modeling maneuvers (regarded as variations of speed and turning rate) of the moving object. These models rely on the unicycle navigation model, suitably augmented with two chains of integrators to account for the unknown speed and turning rate command inputs. Quasi-exact time-discretization of the continuous-time Lambda:Omicron models is also carried out to allow their exploitation in nonlinear recursive filters. Simulation experiments are presented to show the effectiveness of the proposed models as compared to state-of-the-art linear and nonlinear motion models for tracking of strongly maneuvering objects.
2022
25th International Conference on Information Fusion (FUSION)
25th International Conference on Information Fusion
Linköping
04-07 July 2022
Matteo Tesori; Giorgio Battistelli; Luigi Chisci; Alfonso Farina; Graziano Alfredo Manduzio
File in questo prodotto:
File Dimensione Formato  
Fusion-2022.pdf

Accesso chiuso

Tipologia: Pdf editoriale (Version of record)
Licenza: DRM non definito
Dimensione 739.4 kB
Formato Adobe PDF
739.4 kB Adobe PDF   Richiedi una copia

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1280104
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact