Total least squares parameter estimation is an alternative to least squares estimation though much less used in practice, partly due to the absence of eecient recursive algorithms or parallel architectures. Here it is shown how previously developed systolic algorithms/architectures for recursive least squares estimation can be used for recursive total least squares problems. Unconstrained as we...

The Capon algorithm, which was originally proposed for wavenumber estimation in array signal processing, has become a powerful tool for spectral analysis. Over several decades, a significant amount of research attention has been devoted to the estimation of the Capon spectrum. Most of the developed algorithms thus far, however, rely on the direct computation of the inverse of the input correlat...

In this paper, a model-based approach to pel-recursive motion estimation is presented. The derivation of the algorithm is similar to the Wiener-based pel-recursive motion estimation algorithm. However, the proposed algorithm utilizes the spatiotemporal correlations in an image sequence by considering an autoregressive model for the motion compensated frames. Therefore, depending on the support ...

The identification of continuous-time stochastic systems, in particular recursive estimation, is a basic building block for continuous-time stochastic adaptive filtering and control as well, see the works of Van Schuppen, Duncan and Pasik-Duncan. In these papers the underlying stochastic systems is essentially an AR-system, for which the recursive maximum-likelihood (RML) estimation reduces to ...

This paper presents a new approach towards the problem of recursive optical flow estimation from image sequences based on the differential framework proposed by Horn & Schunck. We show that gain is achieved both from computational and accuracy points of view when treating the estimation task recursively. Incorporation of the temporal axis into the estimation process is done by combining a tempo...

We start with estimation of a constant based on several noisy measurements. Suppose we have a resistor but do not know its resistance. So we measure it several times using a cheap (and noisy) multimeter. How do we come up with a good estimate of the resistance based on these noisy measurements? More formally, suppose x = (x1, x2, . . . , xn) T is a constant but unknown vector, and y = (y1, y2, ...