Two-Dimensional DOA Estimation for Planar Array Using a Successive Propagator Method

Abstract

We consider the problem of two-dimensional (2D) direction of arrival (DOA) estimation for planar array, and propose a successive propagator method (PM)-based algorithm. The rotational invariance property of the propagator matrix is exploited to obtain the initial angle estimations, while the accurate estimates can be achieved through successive one-dimensional and local spectrum-peak searches. The proposed algorithm can obtain automatically paired 2D-DOA estimations, and it requires no eigenvalue decomposition of the covariance matrix of received data, which remarkably reduces the computational cost compared with traditional 2D-PM algorithm. In addition, the DOA estimation performance of the proposed algorithm is better than estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm and PM algorithm, and is close to 2D-PM algorithm which requires 2D spectrum-peak search. Numerical simulations demonstrate the effectiveness and improvement of the proposed algorithm.

Direction-Finding Using Co-prime Array under Impulsive Noise

“…In [6], an estimation of signal parameters via the rotational invariance techniques (ESPRIT) method was proposed, and the super-resolution limit of whom was given in [7]. In [8], a successive propagation method without the eigenvalue decomposition operation was proposed to estimate the angles using a planar array. In [9], some fast implementations of the stochastic maximum likelihood method were proposed.…”

Subspace leakage reduced nonuniform sparse Fibonacci‐like planar array to estimate azimuth and elevation angles of both Angle of Arrival and Angle of Departure

“…However, the performance in estimating the angles is not uniform in all directions 1 . In the case of UPA , the antennas with uniform interantenna element spacing arranged in both rows and columns 2 . UCA is a nonlinear structure with a circular arrangement of the antennas and with uniform interantenna element spacing 3,4 .…”

“…Proposal of a nonuniform sparse Fibonacci‐like active antenna selection (NUSFAS) algorithm for the estimation of angles. Different from References 1‐28 which finds azimuth and elevation angles of AoA alone, the proposed method finds azimuth and elevation angles of both AoA and AoD. The sources or users are assumed to be coherent causing the correlation to be increased. Correlated sources decreases the angular resolution performance of the conventional MUSIC algorithm.…”

“…Correlated sources decreases the angular resolution performance of the conventional MUSIC algorithm. The proposed NUSFAS algorithm improves the performance through the selection of active antennas as nonuniform with reduced complexity even at low SNR and fewer snapshots. Subspace leakage reduced NUSFAS (SLR‐NUSFAS) algorithm is proposed for further improving the estimated angle resolution with reduced complexity. The authors have used the Saleh Valenzuela (SV) channel model different from References 1,2,5‐28 for the performance analysis without a kronecker product between AoA and AoD steering vectors and with kronecker product between the azimuth and elevation angles. The authors have derived analytical expressions for subspace leakage between the noise and the signal subspace in two‐dimensional planar array. In addition, they have proved that the subspace leakage produced by NUSFAS is greater than SLR‐NUSFAS. Numerical simulations have been performed for the investigation of the performance of the proposed methods in the 5G massive MU‐MIMO system with UPA.…”

“…1 In the case of UPA, the antennas with uniform interantenna element spacing arranged in both rows and columns. 2 UCA is a nonlinear structure with a circular arrangement of the antennas and with uniform interantenna element spacing. 3,4 The ULA provides 180 • coverage relative to the array axis but UCA provides 360 • of azimuthal coverage.…”