7 research outputs found
The Maximal Eigengap Estimator for Acoustic Vector-Sensor Processing
This paper introduces the maximal eigengap estimator for finding the
direction of arrival of a wideband acoustic signal using a single
vector-sensor. We show that in this setting narrowband cross-spectral density
matrices can be combined in an optimal weighting that approximately maximizes
signal-to-noise ratio across a wide frequency band. The signal subspace
resulting from this optimal combination of narrowband power matrices defines
the maximal eigengap estimator. We discuss the advantages of the maximal
eigengap estimator over competing methods, and demonstrate its utility in a
real-data application using signals collected in 2019 from an acoustic
vector-sensor deployed in the Monterey Bay
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Multi-frequency sparse Bayesian learning for robust matched field processing
The multi-snapshot, multi-frequency sparse Bayesian learning (SBL) processor is derived and its performance compared to the Bartlett, minimum variance distortionless response, and white noise constraint processors for the matched field processing application. The two-source model and data scenario of interest includes realistic mismatch implemented in the form of array tilt and data snapshots not exactly corresponding to the range-depth grid of the replica vectors. Results demonstrate that SBL behaves similar to an adaptive processor when localizing a weaker source in the presence of a stronger source, is robust to mismatch, and exhibits improved localization performance when compared to the other processors. Unlike the basis or matching pursuit methods, SBL automatically determines sparsity and its solution can be interpreted as an ambiguity surface. Because of its computational efficiency and performance, SBL is practical for applications requiring adaptive and robust processing