Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
Direction finding in non-Gaussian impulsive noise environments
Digital Signal Processing
Maximum-likelihood array processing in non-Gaussian noise with Gaussian mixtures
IEEE Transactions on Signal Processing
Acoustic vector-sensor processing in the presence of a reflectingboundary
IEEE Transactions on Signal Processing
Acoustic vector-sensor beamforming and Capon direction estimation
IEEE Transactions on Signal Processing
Identifiability in array processing models with vector-sensorapplications
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Performance bounds for estimating vector systems
IEEE Transactions on Signal Processing
Adaptive eigendecomposition of data covariance matrices based onfirst-order perturbations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Near-field/far-field azimuth and elevation angle estimation using asingle vector hydrophone
IEEE Transactions on Signal Processing
Subspace-based direction-of-arrival estimation using nonparametricstatistics
IEEE Transactions on Signal Processing
A subspace-based direction finding algorithm using fractional lowerorder statistics
IEEE Transactions on Signal Processing
Acoustic vector-sensor array processing
IEEE Transactions on Signal Processing
Multidimensional Systems and Signal Processing
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This paper proposes a new underwater acoustic 2-D direction finding algorithm using two identically oriented vector hydrophones at unknown locations in non-Gaussian impulsive noise. The two applied vector hydrophones are four-component, orienting identically in space with arbitrarily and possibly unknown displacement. Each vector hydrophone has three spatially co-located but orthogonally oriented velocity hydrophones plus another pressure hydrophone. The proposed algorithm employs the spatial invariance between the two vector hydrophones, but requires no a priori information of vector hydrophones' spatial factors and impinging sources' temporal forms. We apply ESPRIT to estimate vector hydrophones manifold and then to pair the x-axis direction cosines with y-axis direction cosines automatically and yield azimuth and elevation angle estimates. We also consider the additive noise be non-Gaussian impulsive, which is often encountered in underwater acoustics applications. Two typical impulsive noise model, Gaussian-mixture noise and symmetric @a-stable (S@aS) noise models are adopted. Instead of using conventional second order correlation of array output data, we define the vector hydrophone array sign covariance matrix (VSCM) for Gaussian-mixture noise and vector hydrophone array fractional lower order moment (VFLOM) matrix for S@aS noise with 1