1. INTRODUCTION
Fast estimation and tracking of the principal or minor subspace of a sequence of random vectors is a major problem in many applications. We can cite, for example, code division multiple access (CDMA) communications, where many multiuser detection algorithms are actually subspace-based [1]. Recently, we presented in [2] a new principal subspace tracker dedicated to time series analysis, which is derived from the SP algorithm by C.E. Davila [3]. This new algorithm, referred to as YAST, reaches the lowest complexity found in the lit-erature, and outperforms classical methods in terms of subspace esti-mation. Moreover, it guarantees the orthonormality of the subspace weighting matrix at each time step. In this paper, we focus on minor subspace analysis (MSA). In the literature, it is commonly admitted that MSA is a more difficult problem than principal subspace analysis (PCA). In particular, the classical Oja algorithm [4] is known to diverge. Some more robust MSA algorithms have been presented in [5]–[9]. However the convergence rate of these algorithms is much lower than that of the classical PCA techniques. Here we propose a version of the YAST algorithm dedicated to MSA, which is shown to have better convergence properties.