Abstract
We propose a low complexity subspace-based direction-of-arrival (DOA) estimation algorithm employing a direct signal space construction method (DSPCM) by subsampling the autocorrelation matrix of a uniform linear array (ULA). Three major contributions of this paper are as follows. First of all, we introduce the method of autocorrelation matrix subsampling which enables us to employ a low complexity algorithm based on a ULA without computationally complex eigenvalue decomposition or singular-value decomposition. Secondly, we introduce a signal vector separation method to improve the distinguishability among signal vectors, which can greatly improve the performance, particularly, in low signal-to-noise ratio (SNR) regime. Thirdly, we provide a root finding (RF) method in addition to a spectral search (SS) method as the angle finding scheme. Through simulations, we illustrate that the performance of the proposed scheme is reasonably close to computationally much more expensive MUSIC- (MUltiple SIgnal Classification-) based algorithms. Finally, we illustrate that the computational complexity of the proposed scheme is reduced, in comparison with those of MUSIC-based schemes, by a factor ofO(N2/K), whereKis the number of sources andNis the number of antenna elements.
Highlights
Subspace-based spectral estimation and direction-of-arrival (DOA) estimation schemes have been widely studied during the last several decades [1,2,3]
We proposed a low complexity subspacebased DOA estimation algorithm for a uniform linear array
To avoid possible lack of distinguishability among the constructed signal space basis vectors, at low signal-to-noise ratio (SNR), we proposed a signal vector separation method by slightly trading off the signal space dimension
Summary
Subspace-based spectral estimation and direction-of-arrival (DOA) estimation schemes have been widely studied during the last several decades [1,2,3]. A 2-dimensional (2D) DOA estimation algorithm based on cross-correlation matrix was proposed in [14] that computes the required projection operators without using ED or SVD. Even though these methods do not require ED or SVD process, they are still computationally burdensome for an array with large antenna elements. We propose a low complexity subspacebased DOA estimation algorithm employing direct signal space construction method (DSPCM) similar to that in [15] but by subsampling the autocorrelation matrix of a ULA. For a real number x, ⌊x⌋ denotes the largest integer that does not exceed x
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