Uni-vector-sensor ESPRIT for multisource azimuth, elevation, and polarization estimation

Abstract

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 polarizationsensitive 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 signalsubspace 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.

Statistical resolution limit for the multidimensional harmonic retrieval model: hypothesis test and Cramér-Rao Bound approaches

“…One can find many estimation schemes adapted to the multidimensional harmonic retrieval estimation problem, see, e.g., [1,2,[4][5][6][7]9,10]. However, to the best of our knowledge, no work has been done on the resolvability of such a multidimensional model.…”

“…This conclude the proof. [2][3][4][5][6][7][8][9] for some practical examples for the multidimensional harmonic retrieval model. c This study can be straightforwardly extended to other norms.…”

“…The main reason is that the multidimensional harmonic retrieval model is able to handle a large class of applications. For instance, the joint angle and carrier estimation in surveillance radar system [2,3], the underwater acoustic multisource azimuth and elevation direction finding [4], the 3-D harmonic retrieval problem for wireless channel sounding [5,6] or the detection and localization of multiple targets in a MIMO radar system [7,8].…”

A Joint Parameter Estimation Method With Conical Conformal CLD Pair Array

“…The problem of estimating signal polarizations along with arrival angles has been discussed previously in many articles [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The first direction-finding algorithms, explicitly exploiting all six electromagnetic components, have been developed by Nehorai and Paldi [1,2] and Li [4], respectively.…”

“…The crossproduct-based direction of arrival (DOA) estimation algorithm was first adapted to ESPRIT by Wong and Zoltowski [5,6]. The uni-vector-sensor ESPRIT and MUSIC algorithm were proposed in [7,8], respectively. In fact, the aforementioned literatures mostly discuss the six collocated and orthogonally oriented electromagnetic sensors, which is called "complete" EMVS.…”