SPICE a sparse covariance based estimation method for array processing教材.pdf

IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 59, NO. 2, FEBRUARY 2011 629
SPICE: A Sparse Covariance-Based Estimation
Method for Array Processing
Petre Stoica, Fellow, IEEE, Prabhu Babu, and Jian Li, Fellow, IEEE
Abstract—This paper presents a novel SParse Iterative Co- a small number of sources exists and therefore that only a few
variance-based Estimation approach, abbreviated as SPICE, to rows of the signal matrix
array processing. The proposed approach is obtained by the
minimization of a covariance matrix fitting criterion and is par-
. . . .
ticularly useful in many-snapshot cases but can be used even in . . . . (2)
single-snapshot situations. SPICE has several unique features not
shared by other sparse estimation methods: it has a simple and
sound statistical foundation, it takes account of the noise in the
data in a natural manner, it does not require the user to make are different from zero. The estimation problem is then to de-
any difficult selection of hyperparameters, and yet it has global cide from the data which rows of the above matrix are
convergence properties. nonzero. Indeed, once this is done, the solution to the location
Index Terms—Array processing, covariance fitting, direction-of- estimation problem, which is usually the main goal of array pro-
arrival (DOA) estimation, sparse parameter estimation. cessing, is immediate : if the row