Abstract
In this paper, we investigate the problem of two-dimensional (2D) direction-of-arrival (DOA) estimation for generalized co-prime planar arrays. The classic multiple signal classification (MUSIC)-based methods can provide a superior estimation performance, but suffer from a tremendous computational burden caused by the 2D spectral search. To this end, we reduce the 2D problem into a one-dimensional (1D) one and propose a reduced dimension partial spectral search estimation method, which can compress the search region into a small 1D sector. Moreover, the proposed method can utilize the full information of the entire array without degrees-of-freedom loss. Furthermore, an iterative approach is also proposed to reduce complexity and improve performance. Simulation results show that the proposed methods can provide improved performance with substantially reduced complexity, as compared to other state-of-the-art methods.
Highlights
Two-dimensional (2D) direction-of-arrival (DOA) estimation has played an important role in the area of array signal processing [1,2,3]
To deal with the computation burden, we reduce the 2D spectral search into a 1D one and propose a 1D PSS-based DOA estimation, where the search region is compressed into a small sector
We compare the performance of the proposed methods with other methods, including 2D multiple signal classification (MUSIC) for a uniform rectangle array, reduced dimensional-based PSS method (RD-PSS) [19] and the stochastic CRB [18]
Summary
Two-dimensional (2D) direction-of-arrival (DOA) estimation has played an important role in the area of array signal processing [1,2,3]. Various methods have been used in radar, sonar and other applications, such as multiple signal classification (MUSIC) [4], quaternion-MUSIC [5] and the estimation of signal parameters via rotational invariance technique (ESPRIT) [6] Among these methods, MUSIC is regarded as one of the most representative techniques due to its high resolution and flexibility for arbitrary arrays. For co-prime planar arrays, a polynomial root finding-based method was proposed in [20], which can avoid the spectral search step. We present a computationally-efficient reduced dimension-based DOA estimation method for generalized co-prime planar arrays. To deal with the computation burden, we reduce the 2D spectral search into a 1D one and propose a 1D PSS-based DOA estimation, where the search region is compressed into a small sector. Simulations have shown that the proposed method can provide superior estimation performance with substantially reduced complexity, as compared with other state-of-the-art methods
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