Computationally efficient two-dimensional capon spectrum analysis

We present a computationally efficient algorithm for computing the 2-D Capo n spectral estimator. The implementation is based on the fact that the 2-D data covariance matrix will have a Toeplitz-Block-Toeplitz structure, with the result that the inverse covariance matrix can be expressed in closed fo rm by using a special case of the Gohberg-Heinig formula that is a function of strictly the forward 2-D prediction matrix polynomials. Furthermore, we present a novel method, based on a 2-D lattice algorithm, to compute the n eeded forward prediction matrix polynomials and discuss the difference in t he so-obtained 2-D spectral estimate as compared with the one obtained by u sing the prediction matrix polynomials given by the Whittle-Wiggins-Robinso n algorithm. Numerical simulations illustrate the improved resolution as we ll as the clear computational gain in comparison to both the well-known cla ssical implementation and the method recently published by Liu et al..