论文部分内容阅读
In this paper,a new method for fast direction-of-arrival(DOA) estimation with no dependance on array configurations is proposed,which is referred to as the symmetrical multiple signal classification(SMUSIC).Unlike the standard MUSIC,the S-MUSIC spatial spectrum is constructed by the intersection of the noise subspace and the conjugate noise subspace,and it hence generates spectral peaks at the true DOAs and the symmetrical virtual DOAs simultaneously.Such a characteristic allows fast DOA estimation by spectral search over only half of the total angular filed-of-view.Therefore,the new approach has a much lower computational complexity than the standard MUSIC.The statistical performance of S-MUSIC is studied and a close-form expression for the MSEs(mean square errors) of DOA estimation by the proposed estimator is derived.Numerical simulations are conducted to demonstrate the effectiveness of the new algorithm and to verify the theoretical analysis,and it is indicated that S-MUSIC makes a trade-off between MSEs and lower computational complexity as well as an improved resolution for closely-spaced sources as compared to the standard MUSIC.
In this paper, a new method for fast direction-of-arrival (DOA) estimation with no dependance on array configurations is proposed, which is referred to as the symmetrical multiple signal classification (SMUSIC) .Unlike the standard MUSIC, the S-MUSIC spatial spectrum is constructed by the intersection of the noise subspace and the conjugate noise subspace, and it is derived spectral peaks at the true DOAs and the symmetrical virtual DOAs simultaneously. Siuch a characteristic allows fast DOA estimation by spectral search over only half of the total angular filed-of-view.Therefore, the new approach has a much lower computational complexity than the standard MUSIC. The statistical performance of S-MUSIC is studied and a close-form expression for the MSEs (mean square errors) of DOA estimation by the proposed estimator is derived. Numerical simulations are conducted to demonstrate the effectiveness of the new algorithm and to verify the theoretical analysis, and it is indicated that S-MUSIC makes a trade-off between MSEs and lower computational complexity as well as an improved resolution for closely-spaced sources as compared to the standard MUSIC.