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

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3 Author(s)
Chan, S.C. ; Dept. of Electr. & Electron. Eng., Hong Kong Univ. ; Zhang, Z.G. ; Zhou, Y.

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

Published in:

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

Date of Conference:

21-24 May 2006

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