Abstract
In this paper, Root-MUSIC algorithm for direction of arrival (DOA) estimation of uncorrelated signals is explored both for uniform linear and uniform circular arrays. The basic problem in Uniform Linear Arrays (ULAs) is Mutual coupling between the individual elements of the antenna array. This problem is reduced in Uniform Circular Arrays (UCAs) because of its symmetric structure. The DOA estimation of uncorrelated signals that have different power levels is simulated on a MATLAB environment. And the noise consider is white across all the array elements. The factors considered for simulation are number of number of snapshots, array elements, radius of circular array, array length, and signal to noise ratio.
Highlights
The performance of smart antenna systems greatly depends on how effectively the direction of arrival (DOA) estimation algorithms estimate directions of the incoming signals
The parameters generally consider for analysing the smart antenna systems are type of antenna array, number of array elements (N), spacing between the elements (d), array length (L=N*d), number of snapshots [3], signal to noise ratio and mutual coupling among the array elements [4]
The conventional MUltiple SIgnal Classification (MUSIC) algorithm fails to estimate the DOAs of highly correlated incoming signals because the covariance matrix obtained by it is singular
Summary
The performance of smart antenna systems greatly depends on how effectively the DOA estimation algorithms estimate directions of the incoming signals. In smart antenna systems by using DOA algorithms the direction of arrival of incoming signals is estimated It includes the direction of original, interfering and multipath signals. Delay-Sum and Capon’s Minimum Variance algorithms are the two most important conventional algorithms which are based on beamforming approaches The subspace methods such as MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT) can split the received signal space into subspaces such as signal subspace and noise subspace. To decrease the computational complexity in conventional MUSIC algorithm, Root-MUSIC algorithm is developed by Barabell in 1983 based on polynomial roots provides high resolution These subspace algorithms exploit the orthogonality between these two subspaces to estimate the DOAs of the incoming signals. The Root-MUSIC algorithm shows higher accuracy and requires less number of computations than the conventional MUSIC algorithm [8]
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