Finite-precision analysis of a covariance algorithm for least squares FIR filtering and AR modeling

A numerically stable, fast, order-recursive algorithm for solving the covariance problem in signal modeling is described. The propagation of finite arithmetic errors as well as data acquisition errors is studied in detail. First, linearization of the main algorithmic recursions is carried out. Then, a suitable transformation converts the resulting state equations of the accumulated errors into their residual form. Subsequently, bounds for the residuals are computed. The derivation of these bounds depends heavily on the Levinson type structure of the algorithm and the low displacement rank of the problem. The main result is that the algorithm is weakly numerically stable. The proposed order-recursive algorithm is subsequently utilized as a block adaptive method. Its performance is also demonstrated by long run simulations