A new adaptive Kalman filter-based subspace tracking algorithm and its application to DOA estimation

Author

Chan, S.C. ; Zhang, Z.G. ; Zhou, Y.

Author_Institution

Dept. of Electr. & Electron. Eng., Hong Kong Univ.

fYear

2006

fDate

21-24 May 2006

Lastpage

132

Abstract

This paper presents a new Kalman filter-based subspace tracking algorithm and its application to directions of arrival (DOA) estimation. An autoregressive (AR) process is used to describe the dynamics of the subspace and a new adaptive Kalman filter with variable measurements (KFVM) algorithm is developed to estimate the time-varying subspace recursively from the state-space model and the given observations. For stationary subspace, the proposed algorithm will switch to the conventional PAST to lower the computational complexity. Simulation results show that the adaptive subspace tracking method has a better performance than conventional algorithms in DOA estimation for a wide variety of experimental condition

Keywords

Kalman filters; adaptive filters; autoregressive processes; direction-of-arrival estimation; state-space methods; DOA estimation; adaptive Kalman filter-based subspace tracking algorithm; autoregressive process; direction of arrival estimation; state-space model; time-varying subspace; Adaptive filters; Bandwidth; Direction of arrival estimation; Kalman filters; Recursive estimation; Resonance light scattering; Signal processing algorithms; State estimation; Switches; Time varying systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on

Conference_Location

Island of Kos

Print_ISBN

0-7803-9389-9

Type

conf

DOI

10.1109/ISCAS.2006.1692539

Filename

1692539