In many applications, the practical usefulness of adaptive arrays is limited by their convergence rate. The adaptively controlled weights in these systems must change at a rate equal to or greater than the rate of change of the external noise field (e.g., due to scanning in a radar if step scan is not used). This convergence rate problem is most severe in adaptive systems with a large number of degrees of adaptivity and in situations where the eigenvalues of the noise covariance matrix are widely different. A direct method of adaptive weight computation, based on a sample covariance matrix of the noise field, has been found to provide very rapid convergence in all cases, i.e., independent of the eigenvalue distribution. A theory has been developed, based on earlier work by Goodman, which predicts the achievable convergence rate with this technique, and has been verified by simulation.
Rapid Convergence Rate in Adaptive Arrays
IEEE Transactions on Aerospace and Electronic Systems ; AES-10 , 6 ; 853-863
1974-11-01
2324817 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Rapid convergence by cascading Applebaum adaptive arrays
IEEE | 1994
|Rapid Convergence by Cascading Applebaum Adaptive Arrays
Online Contents | 1994
|