A fast algorithm for 2-D direction-of-arrival estimation

Abstract

: A computationally e cient method for two-dimensional direction-of-arrival estimation of multiple narrowband sources impinging on the far eld of a planar array i s p r esented. The key idea is to apply the propagator method which only requires linear operations but does not involve a n y eigendecomposition or singular value decomposition as in common subspace techniques such as MUSIC and ESPRIT. Comparing with a fast ESPRIT-based algorithm, it has a lower computational complexity particularly when the ratio of array size to the number of sources is large, at the expense of negligible performance loss. Simulation results are included to demonstrate the performance of the proposed technique.

Direction finding and mutual coupling estimation for uniform rectangular arrays

“…In [5], a polynomial root-finding-based method was proposed using two parallel ULAs, by decoupling the 2-D problem into two 1-D problems to reduce the computational complexity. Another computationally efficient method was proposed in [6], where the propagator method in [7] was employed based on two parallel ULAs. However, this method requires pair matching between the 2-D azimuth and elevation estimation results and may not work effectively for some situations.…”

“…However, this method requires pair matching between the 2-D azimuth and elevation estimation results and may not work effectively for some situations. To overcome the problem in [6], an L-shaped array was employed instead in [8]. Based on such an L-shaped geometry, a 1-D searching algorithm without the need of pair matching was proposed in [9].…”

Two Uniform Linear Arrays for Non-Coherent and Coherent Sources for Two Dimensional Source Localization

“…Recently, several interesting techniques have been developed to resolve the 2D DOA estimation of multiple incoming incident sources [5][6][7][8][9][10][11][12][13][14]. However, most of these techniques are only applicable when the signals are uncorrelated.…”

“…The elevation (θ) is estimated from the Q matrix and azimuth (φ) from the R matrix. The proposed method has several advantages over the methods in [5][6][7][8][9][10][11][12][13][14][15][16]. First, the proposed method does not require spatial smoothing [3,4] which results in a reduction in the computational complexity and cost.…”

“…Fourth, the proposed method utilizes only a single snapshot of the received data to estimate the DOA for incident sources. This significantly reduces the computational load and makes the proposed method a superior candidate for real-time implementation, whereas the methods in [5][6][7][8][9][10][11][12][13][14][15][16] require a large number of snapshots (e.g., 100 to 200) in order to produce a quality estimate. Fifth, computing the QR factorization for the data matrix using Toeplitz structure [17] in the proposed method requires only O((N + 1) 2 ) operations while calculating regular data matrix with dimension (N × N ) using the singular value decomposition (SVD) of the data matrix or the Eigen value decomposition (EVD) of the covariance matrix which have been heavily used by many existing schemes requires O(N 3 ) operations [18].…”