Salem Akkar

Directions of arrival estimation with planar antenna arrays in the presence of mutual coupling

Directions of arrival (DoAs) estimation of multiple sources using an antenna array is a challenging topic in wireless communication. The DoAs estimation accuracy depends not only on the selected technique and algorithm, but also on the geometrical configuration of the antenna array used during the estimation. In this article the robustness of common planar antenna arrays against unaccounted mutual coupling is examined and their DoAs estimation capabilities are compared and analysed through computer simulations using the well-known MUltiple SIgnal Classification (MUSIC) algorithm. Our analysis is based on an electromagnetic concept to calculate an approximation of the impedance matrices that define the mutual coupling matrix (MCM). Furthermore, a CRB analysis is presented and used as an asymptotic performance benchmark of the studied antenna arrays. The impact of the studied antenna arrays geometry on the MCM structure is also investigated. Simulation results show that the UCCA has more robustness against unaccounted mutual coupling and performs better results than both UCA and URA geometries. The performed simulations confirm also that, although the UCCA achieves better performance under complicated scenarios, the URA shows better asymptotic (CRB) behaviour which promises more accuracy on DoAs estimation.

Extended reactance domain algorithms for DoA estimation onto an ESPAR antennas

ABSTRACT Based on an extended reactance domain (RD) covariance matrix, this article proposes new alternatives for directions of arrival (DoAs) estimation of narrowband sources through an electronically steerable parasitic array radiator (ESPAR) antennas. Because of the centro symmetry of the classic ESPAR antennas, an unitary transformation is applied to the collected data that allow an important reduction in both computational cost and processing time and, also, an enhancement of the resolution capabilities of the proposed algorithms. Moreover, this article proposes a new approach for eigenvalues estimation through only some linear operations. The developed DoAs estimation algorithms based on this new approach has illustrated a good behaviour with less calculation cost and processing time as compared to other schemes based on the classic eigenvalues approach. The conducted simulations demonstrate that high-precision and high-resolution DoAs estimation can be reached especially in very closely sources situation and low sources power as compared to the RD-MUSIC algorithm and the RD-PM algorithm. The asymptotic behaviours of the proposed DoAs estimators are analysed in various scenarios and compared with the Cramer-Rao bound (CRB). The conducted simulations testify the high-resolution of the developed algorithms and prove the efficiently of the proposed approach.

MUSIC like algorithms for fast Direction of Arrival estimation

This paper proposes two Unitary MUSIC-like algorithms, requiring only linear operations, for Directions of Arrival (DoAs) estimation problem. The constraints on the proposed algorithms are the same imposed onto the standard MUSIC algorithm allowing high resolution localisation capabilities with a reduced computation cost and lower processing time as compared to the existing schemes. We demonstrate that the introduced Orthogonal Decompositions (OD) technique, for noise subspace estimation, can efficiently replace the requirement of Singular Value Decomposition (SVD) or Eigenvalue Decomposition (EVD) which leads to a reduced computational complexity and makes the DoAs estimation faster while maintaining comparable estimation accuracy. The simulation results confirm that high resolution DoAs estimation can be achieved by the developed methods and prove the validity of our approach.

Directions of Arrival estimation of correlated sources in presence of Mutual coupling

This paper proposes a self-calibration algorithm, that requires only real valued operations, to estimate the Directions of Arrival (DoAs) of correlated sources in the presence of unknown Mutual coupling. Based on the standard MUSIC algorithm and with respect to the same imposed constraints, we develop the Iterative Unitary-MUSIC algorithm that provides superior high resolution localisation capabilities even for correlated sources scenarios with a reduced computations cost as well as a low processing time compared with previous works. In our analysis, the induced Electro-Motive Force (EMF) method is used to model the mutual coupling matrix. Through computer simulations, we demonstrate that our approach has more interesting performances even if all coupling parameters are included and especially when the incoming signals are highly correlated. Moreover, we drive more flexible identifiability conditions for the uniqueness of the estimation solution that required a reduced number of sensors to estimate the same number of parameters compared with previous works.