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Magnitude squared coherence (MSC) is a useful bivariate spectral measure that finds application in a wide variety of fields. In this paper, we develop a nonparametric Capon-based MSC estimator that utilizes a segmented reformulation of the recently introduced iterative adaptive approach (IAA) to provide high resolution MSC spectrum estimates. The proposed(More)
Nuclear quadrupole resonance (NQR) is a solid-state radio frequency (RF) spectroscopic technique, allowing the detection of compounds containing quadrupolar nuclei, a requirement fulfilled by many high explosives and narcotics. The practical use of NQR is restricted by the inherently low signal-to-noise ratio (SNR) of the observed signals, a problem that is(More)
We develop a general robust fundamental frequency estimator that allows for non-parametric inharmonicities in the observed signal. To this end, we incorporate the recently developed multi-dimensional covariance fitting approach by allowing the Fourier vector corresponding to each perturbed harmonic to lie within a small uncertainty hypersphere centered(More)
This work presents a relaxation-based multi-pitch estimation technique for harmonic signals suffering from inharmonicity. Different from most earlier works, the proposed method does not require a priori knowledge of the number of sources present, nor of their respective number of harmonics, or the inharmonicity structure of the expected deviations. Using a(More)
In this paper, we introduce robust versions of the recently introduced Capon- and APES-based Magnitude Square Coherence (MSC) spectral estimators. The estimators exploit the related recent development on robust beamforming to allow for imperfect knowledge of the estimated sample correlation matrix. The resulting estimators are found to yield improved(More)
Estimation of high-resolution multidimensional spectra from unevenly sampled limited sized data sets plays an important role in a large variety of signal processing applications. In this work, we develop a high-resolution non-parametric estimator for unevenly sampled N-dimensional data based on a recently introduced iterative method, the so-called iterative(More)
A number of powerful tools for analyzing linear and nonlinear data sets are based on various spectral measures. In particular, the bispectrum is commonly used for testing Gaussianity and linearity. Due to their inherent robustness to model assumptions, non-parametric estimators of the polyspectra are of particular importance. Unfortunately, the most(More)