Abstract Modi ed covariance Pisarenko harmonic decomposition and reformed Pisarenko harmonic decomposition methods are three closed form frequency estimators which are derived from the linear prediction property of sinusoidal signals In this paper we develop the recursive least squares type realizations of these estimators for a single real tone and their frequency tracking performances are con...

In a number of adaptive filtering applications, non-Wiener effects have been observed for the (normalized) leastmean-square algorithm. These effects can lead to performance improvements over the fixed Wiener filter with the same model structure, and are characterized by dynamic behavior of the adaptive filter weights. Here we investigate whether such non-Wiener effects can also occur in the rec...

Flexible riser is a class of flexible pipes which is used to connect subsea pipelines to floating offshore installations, such as FPSOs (floating production/storage/off-loading unit) and SS (semisubmersible) platforms, in oil and gas production. Flexible risers are multilayered pipes typically comprising an inner flexible metal carcass surrounded by polymer layers and spiral wound steel ligamen...

A framework for obtaining fast RLS algorithms which use a roughly quantized auxiliary input signal for eeciently updating the required adaptation gain vector is presented. The new adaptation procedure is very similar to the conventional fast RLS adaptation. Analytically, it is shown that the lter weights are found by solving almost the same normal equations corresponding to the RLS case. Obtain...

This paper proposes recursive least-squares (RLS) filtering and fixed-point smoothing algorithms with uncertain observations in linear discrete-time stochastic systems. The estimators require the information of the auto-covariance function in the semi-degenerate kernel form, the variance of white observation noise, the observed value and the probability that the signal exists in the observed va...

Abstmct-Recently there seems to have been a resurgence of interest in recursive parameter-bounding algorithms. These algorithms are applicable when the noise is bounded and the bound is known to the user. One of the advantages of such algorithms is that 100% confidence regions (which are optimal in some sense) for the parameter estimates can be obtained at every time instant, rather than asympt...

Estimation problems with bounded, uniformly distributed noise arise naturally in reconstruction problems from over complete linear expansions with subtractive dithered quantization. We present a simple recursive algorithm for such bounded-noise estimation problems. The mean-square error (MSE) of the algorithm is “almost” (1 ), where is the number of samples. This rate is faster than the (1 ) MS...

Cambridge, MA 02139 II. RECURSIVE LEAST SQUARES SIGNAL ANALYSIS The problem of locating the position of individual pulses within a group of overlapping pulses can be simplified by preprocessing the date to reduce the overlap. This paper proposes the use of Recursive Least Squares (RLS) prediction for this purpose. The pulse compression performance of two signals derived from the RLS algorithm i...

The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies in tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tireroad friction coeffic...

Lazy learning is a memory-based technique that, once a query is received, extracts a prediction interpolating locally the neighboring examples of the query which are considered relevant according to a distance measure. In this paper we propose a data-driven method to select on a query-by-query basis the optimal number of neighbors to be considered for each prediction. As an efficient way to ide...