Noncircularity-exploitation in direction estimation of noncircular signals with an acoustic vector-sensor

Noncircular-PARAFAC for 2D-DOA estimation of noncircular signals in arbitrarily spaced acoustic vector-sensor array subjected to unknown locations

In this paper, we propose a noncircular-parallel factor (NC-PARAFAC) algorithm for two-dimensional direction of arrival (DOA) estimation of noncircular signals for acoustic vector-sensor array. The proposed algorithm enhances the angle estimation performance via utilizing the noncircularity of the signals, and it can be suitable for arbitrary array subjected to unknown locations and achieve aut...

New Approaches for Two-Dimensional DOA Estimation of Coherent and Noncircular Signals with Acoustic Vector-sensor Array

Three Dimensional Localization of an Unknown Target Using Two Heterogeneous Sensors

Heterogeneous wireless sensor networks consist of some different types of sensor nodes deployed in a particular area. Different sensor types can measure different quantity of a source and using the combination of different measurement techniques, the minimum number of necessary sensors is reduced in localization problems. In this paper, we focus on the single source localization in a heterogene...

Computationally Efficient Direction Finding for a Mixture of Circular and Strictly Noncircular Sources with Uniform Rectangular Arrays

In this paper, a novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm for the mixed circular and strictly noncircular sources is proposed. A general array model with a mixture of signals is firstly built based on uniform rectangular arrays (URAs), and then, the approach, which uses the rank-reduction-based ROOT-MUSIC, can solve 2D DOA estimation problem. Besides, the theor...

Acoustic correlated sources direction finding in the presence of unknown spatial correlation noise

In this paper, a new method is proposed for DOA estimation of correlated acoustic signals, in the presence of unknown spatial correlation noise. By generating a matrix from the signal subspace with the Hankel-SVD method, the correlated resource information is extracted from each eigen-vector. Then a joint-diagonalization  structure is constructed of the signal subspace and basis it, independent...