Abstract:In the context of passive sources localization using antenna array, the estimation accuracy of elevation, and azimuth are related not only to the kind of estimator which is used, but also to the geometry of the considered antenna array. Although there are several available results on the linear array, and also for planar arrays, other geometries existing in the literature, such as 3D arrays, have been less studied. In this paper, we study the impact of the geometry of a family of 3D models of antenna array on … Show more
“…where, s(m) is the incident signal at time instant m and n(m) is additive complex-valued spatiotemporal white Gaussian noise with a mean of zero and a variance of σ 2 n which are both prior known [33,34,13,11,35,8,36,37,38,39,40,41,1,42,5].…”
This paper proposes a new sensor-array geometry (the 2-circle concentric array geometry), that maximizes the array's spatial aperture mainly for bivariate azimuth-polar resolution of direction-of-arrival estimation problem. The proposed geometry provides almost invariant azimuth angle coverage and oers the advantage of full rotational symmetry (circular invariance) while maintaining an inter-sensor spacing of only an half wavelength (for non-ambiguity withrespect to the Cartesian direction cosines). A better-accurate performance in direction nding of the proposed array grid over a single ring array geometry termed as uniform circular array (UCA) is hereby analytically veried via Cramer-Rao bound analysis. Further, the authors demonstrate that the proposed sensor-array geometry has better estimation accuracy than a single ring array.
“…where, s(m) is the incident signal at time instant m and n(m) is additive complex-valued spatiotemporal white Gaussian noise with a mean of zero and a variance of σ 2 n which are both prior known [33,34,13,11,35,8,36,37,38,39,40,41,1,42,5].…”
This paper proposes a new sensor-array geometry (the 2-circle concentric array geometry), that maximizes the array's spatial aperture mainly for bivariate azimuth-polar resolution of direction-of-arrival estimation problem. The proposed geometry provides almost invariant azimuth angle coverage and oers the advantage of full rotational symmetry (circular invariance) while maintaining an inter-sensor spacing of only an half wavelength (for non-ambiguity withrespect to the Cartesian direction cosines). A better-accurate performance in direction nding of the proposed array grid over a single ring array geometry termed as uniform circular array (UCA) is hereby analytically veried via Cramer-Rao bound analysis. Further, the authors demonstrate that the proposed sensor-array geometry has better estimation accuracy than a single ring array.
“…Based on the simplified CRLB equation the metric function is defined and minimized. A few papers consider only certain array shapes [13] like 3D particular geometry antennas made from uniform linear array (ULA). As it can be seen from the above, these approaches complicate the implementation of the search for the solution of cost functions, because it is necessary to use genetic algorithms.…”
Section: Related Workmentioning
confidence: 99%
“…Several different performance and design criteria have been introduced to be used in obtaining optimal arrays [13][14][15][16][17], we can say the array with the highest bound is optimum in the sense that array is constructed using prespecified performance levels, in our case Cramer-Rao Bounds on error variance and minimum and maximum coordinates in the XY plane. The calculations can be executed manually by means of the presented in the paper simple relationships.…”
In this paper an approach of obtaining optimal planar antenna arrays consisting of omnidirectional sensors is proposed. The novelty of the proposed approach is to apply an exact expression of the Cramer-Rao lower bound for an arbitrary planar antenna array consisting of a number of omnidirectional elements which has been presented in the further chapters of the paper. The obtained formula describes the influence of antenna elements locations on the direction-of-arrival estimation accuracy. It has been shown that the direction-of-arrival accuracy via planar antenna arrays is determined as the sum of squares of differences between all omnidirectional elements coordinates along x- and y-axis. Thus knowing an expected area or sector of signal source it is very easy to calculate optimal arrangement of antenna elements in order to reduce direction-finding errors, because obtained by that way positions gives the best match according to the maximum likelihood criterion. It is worth nothing that such antenna arrays are useful in the way that they allow estimating the coordinates of radio emission sources in the three-dimensional coordinate space, i.e. in azimuth and elevation. In order to confirm the proposed methodology optimal antenna arrays constructed after minimization of the new formulas are researched. It is found out that the new shapes of antenna arrays based on the analytical expressions have better direction-of-arrival accuracy in comparison with the circular ones.
In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D) subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions.
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