DETERMINISTIC PREWHITENING TO IMPROVE SUBSPACE BASED PARAMETER ESTIMATION TECHNIQUES IN SEVERELY COLORED NOISE ENVIRONMENTS

Colored noise is encountered in a variety of signal processing applications. For such applications the prewhitening step becomes essential, since parameter estimation without prewhitening can be severely degraded. Traditionally stochastic prewhitening techniques transform the colored noise into white noise keeping the SNR constant. In this paper, we propose a deterministic approach for subspace prewhitening, where we remove the correlation, which increases the SNR. Consequently, in high noise correlation scenarios, where the subspace is prewhitened by our deterministic approach, there is a significant improvement in the parameter estimation accuracy. The proposed deterministic prewhitening requires knowledge of the noise correlation. Therefore, we also propose solutions to estimate the correlation coefficients.