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Butt N.R.,Lund University | Adalbjornsson S.I.,Lund University | Somasundaram S.D.,SK3 Group | Jakobsson A.,Lund University
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2013

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 around its strictly harmonic counterpart. Within these hyperspheres, we find the best perturbed vectors fitting the covariance of the observed data. The proposed approach provides the estimate of the fundamental frequency in two steps, and, unlike other recentmethods, involves only a single 1-D search over a range of candidate fundamental frequencies. The proposed algorithm is numerically shown to outperform the current competitors under a variety of practical conditions, including various degrees of inharmonicity and different levels of noise. © 2013 IEEE. Source


Somasundaram S.D.,SK3 Group
IEEE Journal of Oceanic Engineering | Year: 2013

In passive sonar, narrowband adaptive beamforming techniques can be exploited to increase the signal-to-interference-plus-noise ratio (SINR), providing that array steering vector (ASV) errors and cross-spectral density matrix (CSDM) estimation errors can be controlled. When beamforming large aperture, many-element arrays in dynamic scenarios, the number of stationary snapshots available for CSDM estimation can be small compared to the number of array elements, leading to the problem of snapshot deficiency. Furthermore, common narrowband approaches become computationally prohibitive for large bandwidths. Here, we exploit the wideband nature of passive sonar signals to alleviate snapshot deficiency and reduce computational complexity. Narrowband robust Capon beamformers (RCBs), which exploit ellipsoidal ASV uncertainty sets to maintain high SINR, are extended to the wideband problem via the steered covariance matrix (STCM) method, yielding wideband RCBs (WBRCBs). To further reduce computational complexity and speed up algorithm convergence, subarray techniques are also incorporated, yielding wideband subarray RCBs (WBSARCBs). These algorithms, which are applicable to arbitrary array geometries, are evaluated using simulated and experimental passive sonar data. © 1976-2012 IEEE. Source


Somasundaram S.D.,SK3 Group
IEEE Transactions on Signal Processing | Year: 2012

In this paper, a novel linearly constrained robust Capon beamformer (LCRCB) framework is proposed. In the LCRCB, linear constraints can be used, e.g., for beampattern control and ellipsoidal array steering vector sets can be exploited, using robust Capon beamforming techniques, e.g., to allow for arbitrary array steering vector errors, such as those arising from calibration errors. The LCRCB is applicable to arbitrary array geometries and can be computed efficiently. For the limiting case that the ellipsoid is a point, we show that the LCRCB coincides with a linearly constrained minimum variance beamformer. To show the utility of the LCRCB, mainbeam and null-pattern control examples are included. © 2012 IEEE. Source


Somasundaram S.D.,SK3 Group | Jakobsson A.,Lund University | Parsons N.H.,SK3 Group
IEEE Transactions on Geoscience and Remote Sensing | Year: 2012

The robust Capon beamformer has been shown to alleviate the problem of signal cancellation resulting from steering vector errors, caused, for example, by calibration and/or angle-of-arrival (AOA) errors, which would, otherwise, seriously degrade the performance of an adaptive beamformer. Here, we examine robust Capon beamforming of multidimensional arrays, where robustness to AOA errors is needed in both azimuth and elevation. It is shown that the commonly used spherical uncertainty sets are unable to control robustness in each of these directions independently. Here, we instead propose the use of flat ellipsoidal sets to control the AOA uncertainty. To also allow for other errors, such as calibration errors, we combine these flat ellipsoids with a higher dimension error ellipsoid. Computationally efficient automatic techniques for estimating the necessary uncertainty sets are derived, and the proposed methods are evaluated using both simulated data and experimental underwater acoustic measurements, clearly showing the benefits of the technique. © 1980-2012 IEEE. Source

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