Joint Source Number Detection and DOA Track Using Particle Filter

Author

Hu De-xiu ; Zhao Yong-jun ; Li Dong-Hai

Author_Institution

Inf. Technol. Univ., Zhengzhou, China

Volume

2

fYear

2010

fDate

13-14 March 2010

Firstpage

541

Lastpage

544

Abstract

Current particle filter assumes that the dimension of state is known and constant and it fails when this suppose does not holds. This paper improves on the particle filter using RJMCMC. The improved particle filter can not only preserve the performance on the Non-Linear and Non-Gaussian condition, but also can be used when the dimension of state is unknown or changing over time. This paper uses the improved particle filter in joint direction-of-arrival(DOA) track and source number detection and makes Simulation which shows that the algorithm is effective.

Keywords

Markov processes; Monte Carlo methods; direction-of-arrival estimation; DOA; RJMCMC; direction-of-arrival; joint source number detection; particle filter; reversible jump Markov Chain Monte Carlo; Current measurement; Equations; Gaussian noise; Least squares approximation; Particle filters; Particle measurements; Particle tracking; Sensor arrays; Sensor systems; State estimation; DOA Track; Particle Filter; RJMCMC; Source Number Detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on

Conference_Location

Changsha City

Print_ISBN

978-1-4244-5001-5

Electronic_ISBN

978-1-4244-5739-7

Type

conf

DOI

10.1109/ICMTMA.2010.761

Filename

5460245