Adaptive High-Resolution Algorithms for Tracking Nonstationary Sources Without the Estimation of Source Number

In this paper, a generalized inflation method which can adaptively and robustly converge to the noise-subspace is proposed to improve the performances of subspace algorithms used for tracking nonstationary sources. This generalized inflation method, which includes an inflation factor developed in the view point of orthogonal projection, preserves the parallel structure for realizations and achieves better performances of convergence and initialization behavior than the inflation method, adaptive PHR algorithms, and other adaptive eigensubspace algorithms when the number of sources is not known. A bound of the inflation factor is also suggested to secure the noise-subspace-only adaptation. The general inflation method in use of weighted-subspace can further improve the tracking performances. Simulations for analyzing the tracking performances of the algorithms are also included.