The convergence rate performance of generalized sidelobe canceller

The least mean square (LMS) algorithm is widely used in adaptive array systems since it can provides both low complexity and robust performance. However, the convergence rate of this gradient-descent-based algorithm is governed by the eigenvalue spread of the autocorrelation matrix. In this paper, the eigenvalue spread ratio (ESR) in a generalized sidelobe canceller (GSC) is derived, It can provide us insight of how the various parameters, related to an adaptive array system, affect the performance of convergence rate. Finally numerical and simulation results are used to verify that the derivation of ESR is correct.