2-D DOA and mutual coupling coefficient estimation for arbitrary array structures with single and multiple snapshots

Elbir, Ahmet M.
Tuncer, Temel Engin
Direction-of-arrival (DOA) estimation for arbitrary array structures in the presence of mutual coupling (MC) is an important problem for antenna arrays. Previous methods in the literature are usually proposed for certain array geometries and show limited performance at low SNR or for small number of snapshots. In this paper, compressed sensing is used to exploit the joint-sparsity of the array model to estimate both DOA and MC coefficients with a single snapshot for an unstructured array where the antennas are placed arbitrarily in space. A joint-sparse recovery algorithm for a single snapshot (JSR-SS) is presented by embedding the source DOA angles and MC coefficients into a joint-sparse vector. A dictionary matrix is defined by considering the symmetricity of the MC matrix for the unstructured antenna array. The proposed method is extended to the multiple snapshots, and the joint-sparse recovery algorithm with multiple snapshots (JSR-MS) is developed. A new joint-sparsity structure, namely, joint-block-sparsity is introduced to take advantage of the structure in the composite matrix involving both DOA and MC coefficients. 1-D and 2-D DOA estimation performance of the proposed methods is provided in comparison to the conventional sparse recovery techniques, subspace methods, and the Cramer-Rao lower bound. It is shown that the proposed methods perform significantly better than the alternative methods.


2-D DOA estimation in case of unknown mutual coupling for multipath signals
Filik, Tansu; Tuncer, Temel Engin (2016-01-01)
In this paper, two dimensional (2-D) direction-of-arrival (DOA) estimation problem in case of unknown mutual coupling and multipath signals is investigated for antenna arrays. A new technique is proposed which uses a special array structure consisting of parallel uniform linear array (PULA). PULA structure is complemented with auxiliary antennas in order to have a structured mutual coupling matrix (MCM). MCM has a symmetric banded Toeplitz structure which allows the application of the ESPRIT algorithm for 2...
Elbir, Ahmet M.; Tuncer, Temel Engin (2015-09-04)
Direction-of-arrival (DOA) estimation in the presence of mutual coupling and coherent signals is a hard task for arbitrary sensor arrays including uniform circular array (UCA). While the coherent sources can be resolved using spatial smoothing algorithms for uniform linear and rectangular arrays, it cannot be applied to UCA. In this paper, a new technique is proposed for DOA estimation in UCA using a single snapshot. Joint-sparse recovery algorithm is proposed where the source signal spatial directions and ...
2-D Navier-Stokes solution method with overset moving grids
Tuncer, İsmail Hakkı (1996-01-01)
A simple, robust numerical algorithm to localize moving boundary points and to interpolate unsteady solution variables across 2-D, arbitrarily overset computational grids is presented. Overset grids are allowed to move in time relative to each other. The intergrid boundary points are localized in terms of three grid points on the donor grid by a directional search algorithm. The parameters of the search algorithm give the interpolation weights at the localized boundary point. The method is independent of nu...
A Fast and Automatically Paired 2-Dimensional Direction-of-Arrival Estimation Using Arbitrary Array Geometry
Filik, T.; Tuncer, Temel Engin (2009-04-11)
A new approach is proposed for two-dimensional (2-D) direction-of-arrival (DOA) estimation with arbitrary array geometries, which is based on array interpolation. The method provides automatically paired source azimuth and elevation angle estimates. Furthermore it is possible to estimate D sources with D + 1 sensor 2-D array interpolation errors are minimized by using Wiener formulation. Proposed method is applied to the two planar arrays; uniform circular array (UCA) and uniform isotropic (IU) V-shaped arr...
Two-mode probabilistic distance clustering
Caner, Yağmur; İyigün, Cem; Department of Industrial Engineering (2021-7-29)
Probabilistic Distance Clustering (PDC) is a soft clustering technique constructed around some axioms. It is a center-based approach and assigns each data point to multiple clusters with a membership probability. The PDC is applicable for one-mode data sets, where each data points’ quantitative or qualitative values over each feature are stored. This study focuses on PDC and consists of two main contributions. Firstly, the relevance of PDC to some other probabilistic models in the literature is examined. We...
Citation Formats
A. M. Elbir and T. E. Tuncer, “2-D DOA and mutual coupling coefficient estimation for arbitrary array structures with single and multiple snapshots,” DIGITAL SIGNAL PROCESSING, pp. 75–86, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48604.