DOA Estimation Based on Eigenvalue Reconstruction of Noise Subspace
Fang Qing-yuan① Han Yong① Jin Ming① Song Li-zhong①② Qiao Xiao-lin①
①(School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China) ②(State Key Laboratory of Millimeter Waves, Nanjing 210096, China)
Abstract：This paper proposes an Eigenvalue Reconstruction method in Noise Subspace (ERNS) for Direction of Arrival DOA estimation with high resolution, provided that the powers of sources are different. The noise subspace eigenvalues belonging to the covariance matrix of received signals, obtained by EigenValue Decomposition (EVD), are modified to construct a new covariance matrix with respect to virtual source. The noise subspace eigenvalues corresponding to the new covariance matrix remain the same as before they are modified. The invariance of the noise subspace is utilized to estimate the DOA of emitters. The theory and process of ERNS algorithm are provided, at the same time, the theory and performance of ERNS algorithm is validated by computer simulations. The simulation results show that the ERNS algorithm has a better performance in successful probability of weak signal estimation compared with other subspace methods and MUSIC algorithm.
方庆园, 韩勇, 金铭, 宋立众, 乔晓林. 基于噪声子空间特征值重构的DOA估计算法[J]. 电子与信息学报, 2014, 36(12): 2876-2881.
Fang Qing-Yuan, Han Yong, Jin Ming, Song Li-Zhong, Qiao Xiao-Lin. DOA Estimation Based on Eigenvalue Reconstruction of Noise Subspace. , 2014, 36(12): 2876-2881.
2014, Vol. 36 
