This paper presents an adaptive estimator, and its practical implementations, of the complete noise or signal subspace of a sample covariance matrix. The general formulation of the proposed estimator results from an asymptotic argument which shows the signal or noise subspace computation to be equivalent to a constrained gradient search procedure. A highly parallel algorithm, denoted the inflation method, is introduced for the estimation of the noise subspace. The simulation results of these adaptive estimators show that the adaptive subspace algorithms perform substantially better than Thompson’s adaptive version of Pisarenko’s technique  in estimating frequencies or directions of arrival (DOA) of plane waves. For tracking nonstationary parameters, the simulation results also show that the adaptive subspace algorithms are better than direct eigendecomposition methods for which computational complexity is much higher than the adaptive versions.
|Original language||English (US)|
|Number of pages||11|
|Journal||IEEE Transactions on Acoustics, Speech, and Signal Processing|
|State||Published - Feb 1988|
Bibliographical noteFunding Information:
Manuscript received February 26, 1987; revised September 27, 1987. This work was supported in part by the National Science Foundation under Grant ECS-8414316, and by the SDIO/IST, managed by the Office of Naval Research, under Contract N00014-86-k-0410. The authors are with the Department of Electrical Engineering, University of Minnesota, Minneapolis, MN 55455. IEEE Log Number 8718438.