RESUMO
Time-delay estimation (TDE), which measures the relative time delay between different receivers, is a fundamental approach for identifying, localizing, and tracking radiating sources. The generalized cross-correlation method is the most popular and is well explained in a landmark paper by Knapp and Carter [(1976). IEEE Trans. Acoust. Speech Signal Process. 24(4), 320-327]. Adaptive eigenvalue decomposition- (EVD) based algorithms have also been developed to improve TDE performance, especially in reverberant environments. This paper extends the adaptive EVD algorithm to utilize the sparsity in transfer channel between source and receivers. Two estimation algorithms based on the log-sum and lp-norm penalized minor component analysis by excitatory and inhibitory learning rules is proposed. In addition, simulations with uncorrelated, correlated noise and reverberation for several signal-to-noise ratios are performed to show the improved estimation performance in noise and reverberation.
RESUMO
In this paper an [Formula: see text]-regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed [Formula: see text]-RTLS algorithm is an RLS like iteration using the [Formula: see text] regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed [Formula: see text]-regularized RTLS for the sparse system identification setting.