Robust implementation of the MUSIC algorithm

The problem of estimating frequencies of sinusoids in noise has been studied intensively by the signal processing community during the last decades. Traditionally high resolution subspace-based techniques suffer from high computational complexity, and generally sensitive to the colored noise. We present here a frequency-domain based subspace parameter estimation algorithm termed frequency-selective MUltiple SIgnal Classification (F-MUSIC) that is based on the signal and noise subspace orthogonality property. The method is computationally efficient in providing estimates in the selected subband compared to the classic MUSIC. The performance of F-MUSIC is evaluated and compared to both MUSIC and Cramer-Rao lower bound (CRLB). In a low signal to noise ratio (SNR) with colored noise scenarios, F-MUSIC outperforms MUSIC.