Subspace-constrained SCORE algorithms
T.E. Biedka · 2002
The SCORE algorithms have been shown to be capable of blindly extracting a desired signal in the presence of unknown noise and interference by exploiting the cyclostationarity of the signal of interest. An analysis of SCORE is presented which demonstrates that, for fixed collect time, the output SINR degrades as the number of sensors increases. The best performance is obtained when the number of sensors equals the number of incident signals. A solution to this problem is presented which involves solving for the SCORE weight vectors subject to the constraint that they lie in the signal subspace of the observed data correlation matrix. It is shown that incorporation of this constraint improves the convergence rate when the signal subspace exists and may be accurately estimated. The effect of rank estimation error is also considered. >