This paper introduces a novel eigenstructure-based algorithm uni-vector-sensor ESPRIT that yields closed-form direction-of-arrival (DOA) estimates and polarization estimates using one electromagnetic vector sensor. A vector sensor is composed of six spatially co-located nonisotropic polarization-sensitive antennas, measuring all six electromagnetic field components of the incident wave field. Uni-vector-sensor ESPRIT is based on a matrix-pencil pair of temporally displaced data sets collected from a single electromagnetic vector sensor. The closed-form parameter estimates are obtained through a vector cross-product operation on each decoupled signal-subspace eigenvector of the data correlation matrix. This method exploits the electromagnetic sources' polarization diversity in addition to their spatial diversity, requires no a priori knowledge of signal frequencies, suffers no frequency-DOA ambiguity, pairs automatically the x-axis direction cosines with the y-axis direction cosines, eliminates array interelement calibration, can resolve up to five completely polarized uncorrelated monochromatic sources from near field or far field. It impressively out-performs an array of spatially displaced identically polarized antennas of comparable array-manifold size and computational load.

Aperture extension (interferometry baseline extension) is achieved in this novel ESPRIT-based two-dimensional (2-D) arrival angle estimation scheme using a sparse (a.k.a., thin or thinned) uniform rectangular array of electromagnetic vector sensors spaced much farther apart than a half-wavelength. An electromagnetic vector sensor is composed of six spatially co-located, orthogonally oriented, diversely polarized antennas, distinctly measuring all six electromagnetic-field components of an incident multisource wavefield. Each incident source's direction of arrival (DOA) is estimated from the source's electromagnetic-field vector component and serves as a coarse reference to disambiguate the cyclic phase ambiguities in ESPRIT's eigenvalues when the intervector sensor spacing exceeds a half wavelength. Simulations demonstrate the significant performance gain realizable by this method for radar and wireless mobile fading-channel communications.

A sparse uniform Cartesian-grid array suffers cyclic ambiguity in its Cartesian direction-cosine estimates due to the spatial Nyquist sampling theorem. The proposed MUSIC-based or MODE-based algorithm improves and generalizes previous disambiguation schemes that populate the thin array grid with identical subarrays-such as electromagnetic vector sensors, underwater acoustic vector hydrophones, or half-wavelength spaced subarrays.

This paper introduces a new underwater acoustic eigenstructure ESPRIT-based algorithm that yields closed-form direction-of-arrival (DOA) estimates using a single vector hydrophone. A vector hydrophone is composed of two or three spatially co-located but orthogonally oriented velocity hydrophones plus another optional co-located pressure hydrophone. This direction finding algorithm may (under most circumstances) resolve up to four uncorrelated monochromatic sources impinging from the near-field or the far-field, but it assumes that all signal frequencies are distinct. It requires no a priori knowledge of the signals' frequencies, suffers no frequency-DOA ambiguity, and pairs automatically the x-axis direction cosines with the y-axis direction cosines. It significantly outperforms an array of spatially displaced pressure hydrophones of comparable array-manifold size and computational load but may involve more complex hardware. This work also derives new Cramer-Rao bounds (CRBs) for various vector hydrophone constructions of arrival angle estimates for the incident uncorrelated sinusoidal signals corrupted by spatio-temporally correlated additive noise.

A novel self-initiating multiple signal classification (MUSIC)-based direction-finding (DF) and polarization-estimation algorithm in spatio-polarizational beamspace is presented for an arbitrarily spaced array of identically oriented electromagnetic vector sensors. An electromagnetic vector sensor, already commercially available, is composed of six colocated, but diversely polarized, antennas distinctly measuring all six electromagnetic-field components of a multisource incident wave field. This proposed algorithm: (1) exploits the incident sources' polarization diversity; (2) decouples the estimation of the sources' arrival angles from the estimation of the sources' polarization parameters; (3) uses ESPRIT on pairs of vector sensors to self-generate coarse estimates of the arrival angles to start off its MUSIC-based iterative search without any a priori information on the incident sources' parameters; (4) estimate the sources' polarization states; and (5) automatically pairs the x-axis direction-cosine estimates with the y-axis direction-cosine estimates and with the polarization estimates. Monte Carlo simulation results verify the efficacy of the proposed method.