Two dimensional angle of arrival estimation using minimum sparse ruler based rectangular array of antennas

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One important application in the field of adaptive antenna array processing used in recent wireless communications systems, such as cognitive radio applications, is the direction of arrival (DoA) estimation. The existing works suggest that the N x × N y two dimensional (2-D) antenna array is almost surely able to recover up to ⌈N x /2⌉ ⌈N y /2⌉ two dimensional (2-D) DoA. In this paper, a new 2-D (azimuth and elevation) DoA estimation method using a minimum sparse ruler based rectangular array of antenna is evaluated. The minimal sparse ruler is used to determine which antennas that have to be deactivated and which antennas that should be remain active. Therefore, it is possible to deactivate some antennas in the uniform rectangular array (URA) leading to a sparse rectangular array (SpRA). While minimizing the reduction in the quality of the resulting DoA estimation with SpRA, the selection and averaging procedure are adopted to tackle these elements. This approach is possible for uncorrelated sources as the covariance matrix of the impinging signals on the URA contains redundant elements. The selection and averaging procedures are adopted to tackle these elements. These steps are followed by the execution of the MUSIC algorithm to compute the 2-D DoA estimates. The simulation study shows that it is possible to employ only 25-antennas in SpRA in order to estimate the azimuth (ϕ) and the elevation (θ) angles of up to 19 sources. The combinations of (ϕ) and (θ) is drawn from the range of 00 ≤ ϕ ≤1800 and 00 ≤ θ ≤ 900. The separation in azimuth and elevation angles between sources is at least 100.