Title :
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
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;
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
DOI :
10.1109/ICMTMA.2010.761
