Two-dimensional DOA estimation of coherent sources using two parallel uniform linear arrays

Abstract

A novel two-dimensional (2-D) direction-of-arrival (DOA) estimation approach based on matrix reconstruction is proposed for coherent signals impinging on two parallel uniform linear arrays (ULAs). In the proposed algorithm, the coherency of incident signals is decorrelated through two equivalent covariance matrices, which are constructed by utilizing cross-correlation information of received data between the two parallel ULAs and the changing reference element. Then, the 2-D DOA estimation can be estimated by using eigenvalue decomposition (EVD) of the new constructed matrix. Compared with the previous works, the proposed algorithm can offer remarkably good estimation performance. In addition, the proposed algorithm can achieve automatic parameter pair-matching without additional computation. Simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

Two‐dimensional DOA estimation for noncoherent and coherent signals using subspace approach

“…In order to show the performance of the proposed algorithm, computer simulations are carried out. The results are compared with those of 2D DOA estimation algorithms for two parallel ULAs such as the FBSS‐DPR, the OPADE, and matrix reconstruction (MR) method . The root‐mean‐square error (RMSE), defined as and , is applied as a criterion to measure the estimation performance with respect to variables such as SNR and snapshots.…”

“…The problem of two‐dimensional (2D) direction‐of‐arrivals (DOAs) estimation of a mixture of noncoherent and coherent narrowband signals has attracted a lot of interest in many practical application scenarios such as radar, sonar, and wireless communication. Compared with the maximum likelihood (ML) method in the works of Sheinvald et al and Stoica and Nehorai, the subspace‐based algorithms (eg, other works) can offer a good trade‐off between the performance and computational complexity and have attained considerable attentions.…”

Cyclostationarity-based DOA estimation algorithms for coherent signals in impulsive noise environments

“…To address cyclostationary signal coherence phenomena in the impulsive noise environment, we introduce two spatial smoothing-based fractional lower-order cyclic algorithms. The performance of the MUSIC and ES-PRIT algorithms can be drastically improved compared to that of the ordinary MUSIC and ESPRIT algorithms by using the Hermitian sample covariance in DOA estimation [7,8] is divided into L uniformly overlapping subarrays of dimension Q (Q > K), in such a way that each sub-array shares all but one of its sensors with an adjacent sub-array [6]. The signal of the lth sub-array is…”

“…Evans and Shane et al proposed derived spatial smoothing techniques for the DOA estimation of coherent signals; in the study, a uniform linear array was divided into several subarrays, and the covariance matrices of the subarrays were then calculated and averaged together [4]. Additionally, subspace smoothing, modified smoothing, and other smoothing methods have been proposed [5][6][7][8]. A new formulation of the Khatri-Rao has been used to estimate DOA which can cope with more coherent signals than classical multiple signal classification (MUSIC) with the spatial smoothing [9].…”