一种多分辨率DOA估计的小波包算法

根据MUSIC算法在信号波达方向(DOA)较近或相干时性能迅速下降的情况,提出了一种多分辨率信号波达方向估计的小波包算法基于子带分解的MUSIC算法(SB MUSIC)·算法对全带信号进行子带分解,并根据最优基选取准则选择最优叶节点,然后对每个叶节点应用MUSIC算法进行谱估计·研究表明子带分解具有提高信噪比(SNR)和放大频率间隔等优点·考虑到算法应用时只有全带频率才有意义,提出了子带频率向全带频率映射的方法·仿真证明,SB MUSIC不仅提高了特征子空间方法的分辨率,而且在子带划分适当时,具有一定的信号去相关能力,并且子带谱的估计成功率和谱平度都高于全带谱·

Novel Multiresolution DOA Estimation Method with Wavelet Packets

A novel wavelet packets-based DOA (direction of arrival) estimation method is proposed for the rapid performance degradation of typical multiple signal classification(MUSIC) algorithm in the scenario where different signals' DOA are close together even coherent, i.e., the subband-based MUSIC algorithm (SB-MUSIC). The SB-MUSIC decomposes the fullband signal into subbands and chooses the optimal leaf nodes in accordance to the best basis criterion. Then, the MUSIC method is applied to every leaf node chosen so as to estimate individually the subband spectrum. The advantages of subband spectra, such as improving SNR and amplifying frequency spacing, are analyzed taking account of that when using SB-MUSIC only the fullband makes sense, thus giving a way for frequency mapping from subband back to fullband. Simulation results showed that SB-MUSIC will not only improve the resolution of classical eigenstructure-based methods but also decouple the coherent signals if the fullband is properly decomposed into subbands. In particular, the rate of success of estimation and spectral flatness measure (SFM) of subband are obviously higher than that of fullband.