Adaptive coherent-signal subspace algorithm for direction estimation and tracking

A new adaptive algorithm is proposed for adjusting the coherent noise subspace of the coherently averaged covariance matrix (CACM) by a constrained least-squares search. The proposed estimator possesses a superior rate of convergence and good tracking properties. All simulations confirm this prediction. The algorithm incurs greater computational cost at each iteration and has a tendency to become numerically unstable. Fortunately, the use of double-precision arithmetic can eliminate the instability problem. also the systolic array implementation developed in recent years may achieve real-time processing