A Low Complexity Mixed Sources Localization Algorithm Without Spectral Peak Search
Yanjie Yang, Heping Shi, Jianfeng Chen, Shujian Wang · 2023
Based on high-order cumulant (HOC) and root-MUSIC theory, a mixed sources localization algorithm with low complexity is presented in this paper. Although having strong estimation performance, the majority of the existing localization algorithm have a heavy computational burden. The presented algorithm successfully reduces the computational burden by substituting the polynomial rooting for multiple signal classification (MUSIC) spectral peak searching. The roots of polynomial around the unit circle, which contains the Direction-of-Arrival (DOA) information of far-field source (FFS), are solved first. Then a HOC matrix is constructed, and the near-field source (NFS) component is extracted via matrix difference method. The DOA of NFS is obtained by combining the forward-backward spatial smoothing and polynomial rooting, and finally the NFS range parameter is estimated using the root MUSIC as well. The proposed algorithm effectively decreases the computational complexity by sacrificing a small part of the estimation performance within an acceptable range. The good estimation performance and low complexity are demonstrated by numerical simulations.