prof. dr hab. inż. Maciej Niedźwiecki | Gdańsk University of Technology

In this paper we propose a solution to the problem of tracking quasi-periodically varying systems based on the local basis function (LBF) approach. Within this framework, parameter trajectories are locally approximated using linear combinations of specific functions of time known as basis functions. We derive both bias and variance characteristics of LBF estimators. Additionally, we demonstrate that the computational burden associated...

When parameters of mobile telecommunication channels change rapidly, classical adaptive filters, such as exponentially weighted least squares algorithms or gradient algorithms, fail to estimate them with sufficient accuracy. In cases like this, one can use identification methods based on explicit models of parameter changes such as the method of basis functions (BF). When prior knowledge about parameter changes is available the...

While local basis function (LBF) estimation algorithms, commonly used for identifying/tracking systems with time-varying parameters, demonstrate good performance under the assumption of normally distributed measurement noise, the estimation results may significantly deviate from satisfactory when the noise distribution is of impulsive nature, for example, heavy-tailed or corrupted by outliers. This paper introduces a computationally...

The problem of noncausal identification of a time-varying linear system subject to both smooth and occasional jump-type changes is considered and solved using the preestimation technique combined with the basis function approach to modeling the variability of system parameters. The proposed estimation algorithms yield very good parameter tracking results and are computationally attractive.

When parameters of wireless communication channels vary at a fast rate, simple estimation algorithms, such as weighted least squares (WLS) or least mean squares (LMS) algorithms, cannot estimate them with the accuracy needed to secure the reliable operation of the underlying communication systems. In cases like this, the local basis function (LBF) estimation technique can be used instead, significantly increasing the achievable...