The presence of unknown mutual coupling between array elements is knownto significantly degrade the performance of most high-resolution direction of arrival (DOA)estimation algorithms. In this paper, a robust subspace...The presence of unknown mutual coupling between array elements is knownto significantly degrade the performance of most high-resolution direction of arrival (DOA)estimation algorithms. In this paper, a robust subspace-based DOA estimation and arrayauto-calibration algorithm is proposed for uniformly linear array (ULA), when the arraymutual coupling is present. Based on a banded symmetric Toeplitz matrix model for themutual coupling of ULA, the algorithm provides an accurate and high-resolution DOAestimate without any knowledge of the array mutual couplings. Moreover, a favorableestimate of mutual coupling matrix can also be achieved simultaneously for arrayauto-calibration. The algorithm is realized just via one-dimensional search or polynomialrooting, with no multidimensional nonlinear search or convergence burden involved. Theproblem of parameter ambiguity, statistically consistence and efficiency of the newestimator are also analyzed. Monte-Carlo simulation results are also provided todemonstrate the effectiveness and behavior of the proposed algorithm.展开更多
文摘The presence of unknown mutual coupling between array elements is knownto significantly degrade the performance of most high-resolution direction of arrival (DOA)estimation algorithms. In this paper, a robust subspace-based DOA estimation and arrayauto-calibration algorithm is proposed for uniformly linear array (ULA), when the arraymutual coupling is present. Based on a banded symmetric Toeplitz matrix model for themutual coupling of ULA, the algorithm provides an accurate and high-resolution DOAestimate without any knowledge of the array mutual couplings. Moreover, a favorableestimate of mutual coupling matrix can also be achieved simultaneously for arrayauto-calibration. The algorithm is realized just via one-dimensional search or polynomialrooting, with no multidimensional nonlinear search or convergence burden involved. Theproblem of parameter ambiguity, statistically consistence and efficiency of the newestimator are also analyzed. Monte-Carlo simulation results are also provided todemonstrate the effectiveness and behavior of the proposed algorithm.
