In this paper, multiple sensor measurement update is studied for a random matrix model. Four different updates are presented and evaluated: three updates based on parametric approximations of the extended target state probability density function and one update based on a Rao-Blackwellized (RB) particle approximation of the state density. An extensive simulation study shows that the RB particle approach shows best performance, at the price of higher computational cost, compared to parametric approximations.

Multiple Sensor Measurement Updates for the Extended Target Tracking Random Matrix Model

Vivone G.
;
Braca P.;
2017-01-01

Abstract

In this paper, multiple sensor measurement update is studied for a random matrix model. Four different updates are presented and evaluated: three updates based on parametric approximations of the extended target state probability density function and one update based on a Rao-Blackwellized (RB) particle approximation of the state density. An extensive simulation study shows that the RB particle approach shows best performance, at the price of higher computational cost, compared to parametric approximations.
2017
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4725330
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 29
  • ???jsp.display-item.citation.isi??? 24
social impact