A segmented iterative approach to estimation of the coherence spectrum from unevenly sampled sequences

We develop a non-parametric Capon-based magnitude squared coherence (MSC) estimator that utilizes a segmented reformulation of the recently introduced iterative adaptive approach (IAA) to provide high resolution MSC spectrum estimates. The proposed estimator, termed segmented-IAA-MSC (or SIAA-MSC, for short), allows for unevenly sampled data as well as for sequences with arbitrarily missing samples. The estimator first uses segmented-IAA to find accurate estimates of the auto- and cross-covariance matrices of the given sequences. These estimates are then used in a Capon-based MSC estimator reformulated to allow for non-uniformly sampled sequences. The proposed SIAA-MSC estimator is found to yield improved estimates as compared to the more commonly used least squares Fourier transform (LSFT) based MSC estimator.