In this work, the problem of joint direction-of-arrival and fundamental frequency estimation for multi-channel harmonic sinusoidal signals is addressed. Different from the conventional optimal filtering method, we estimate the covariance matrix with the 2-D iterative adaptive approach, which is based on a single snapshot. In addition, to improve the estimation accuracy for the off-grid sources, a relaxation technique is utilized. Then, joint estimation is conducted on this covariance matrix estimate with the optimal filtering method. As a result, the relaxed iterative adaptive approach - optimal filtering method is devised. Statistical evaluation with synthetic signals shows the accurate performance of the proposed method compared with the Cramér-Rao lower bound.

In this paper, the problem of fundamental frequency estimation for real harmonic sinusoids is addressed. By making use of the subspace technique and Markov-based eigenanalysis, an optimally weighted harmonic multiple signal classification (OW-HMUSIC) estimator is devised. The fundamental frequency estimates are computed in an iterative manner. The performance of the proposed method is derived. Computer simulations are performed to compare the proposed approach with nonlinear least squares and HMUSIC methods as well as Cramér-Rao lower bound.