Abstract
Direction finding has been extensively studied over the past decades and a number of algorithms have been developed. In direction finding, theoretic performance prediction is a fundamental problem. This paper addresses the performance analysis issue of interferometer-based 2D angle of arrival estimation using uniform circular array (UCA). We propose an analytic method for performance analysis of interferometer in the presence of Gaussian or uniform error in phase measurement of incident signal on each sensor. The analytic mean square error (MSE), which is approximately equal to the MSE of actual interferometer-based DOA estimation, is derived via Taylor expansion and approximation. The derived analytic MSE is useful for predicting how the MSE of the interferometer-based DOA estimation algorithm is dependent on the phase measurement error.
Highlights
An analytic mean square error (MSE) due to phase measurement error for interferometer direction-of-arrival (DOA) estimation algorithm is derived in this paper
Our contribution in this paper lies in a reduction in computational cost in getting the MSE of an existing interferometer algorithm by adopting analytic approach, rather than the Monte Carlo simulation-based MSE under measurement uncertainty due to an additive Gaussiandistributed noise or uniformly-distributed noise
To see how the azimuth and elevation MSEs are dependent on true azimuth and true elevation of the incident signal for array geometry which is not uniform circular array, rectangular planar array is considered
Summary
An analytic MSE (mean square error) due to phase measurement error for interferometer direction-of-arrival (DOA) estimation algorithm is derived in this paper. In recent previous studies [1,2,3], we have proposed how the closed-form expressions of the MSE of various DOA estimation algorithms can be analytically obtained. In [7], algorithm for removing phase ambiguity in the interferometer DOA estimation has been proposed and validated for three antenna system and four antenna system. Various random variables can be adopted to model phase errors. Phase error due to the discrepancy between actual cable length and nominal cable length can be more adequately modeled as Gaussian random variables since small discrepancies in cable length occurs more frequently than large discrepanccies in cable length
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