<p> This thesis investigates the application of multiple model estimation algorithms to the problem of channel equalization for digital data transmission and channel tracking for space-time block coded systems with non-Gaussian additive noise. Recently, a network of Kalman filters (NKF) has been reported for the equalization of digital communication channels based on the approximation of the a posteriori probability density function of a sequence of delayed
symbols by a weighted Gaussian sum. A serious drawback of this approach is that the number
of Gaussian terms in the sum increases exponentially through iterations. In this thesis,
firstly, we have shown that the NKF-based equalizer can be further improved by considering
the interactions between the parallel filters in an efficient way. To this end, we take resort to
the Interacting Multiple Model (IMM) estimator widely used in the area of multiple target
tracking. The IMM is a very effective approach when the system exhibits discrete uncertainties
in the dynamic or measurement model as well as continuous uncertainties in state
values. A computationally feasible implementation based on a weighted sum of Gaussian
approximation of the density functions of the data signals is introduced. Next, we present
an adaptive multiple model blind equalization algorithm based on the IMM estimator to
estimate the channel and the transmitted sequence corrupted by intersymbol interference
and noise. It is shown through simulations that the proposed IMM-based equalizer offers substantially improved performance relative to the blind equalizer based on a (static or non-interacting) network of extended Kalman filters. It obviates the exponential growth of the
state complexity caused by increasing channel memory length. The proposed approaches
avoid the exponential growth of the number of terms used in the weighted Gaussian sum
approximation of the plant noise making it practical for real-time processing.</p> <p> Finally, we consider the problem of channel estimation and tracking for space-time block coded systems contaminated by additive non-Gaussian noise. In many practical wireless channels in which space-time block coding techniques may be applied, the ambient noise is likely to have an impulsive component that gives rise to larger tail probabilities than is predicted by the Gaussian model. Although Kalman filters are often used in practice to track the channel variation, they are notoriously sensitive to heavy-tailed outliers and model mismatches resulting from the presence of impulsive noise. Non-Gaussian noise environments require the modification of standard filters to perform acceptably. Based on the coding/decoding technique, we propose a robust IMM algorithm approach in estimating time-selective fading channels when the measurements are perturbed by the presence of impulsive noise. The impulsive noise is modeled by a two terms Gaussian mixture distribution. Simulations demonstrate that the proposed method yields substantially improved performance compared to the conventional Kalman filter algorithm using the clipping or localization approaches to handle impulses in the observation. It is also shown that IMM-based approach performs robustly even when the prior information about the impulsive noise is not known exactly.</p> / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20764 |
| Date | 09 1900 |
| Creators | Kamran, Ziauddin M. |
| Contributors | Kirubarajan, T., Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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