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This paper presents a novel approach to predict the Internet end-to-end delay using multiple-model (MM) methods. The basic idea of the MM method is to assume the system dynamics can be described by a set of models rather than a single one; by running a bank of filters (each corresponds to a certain model in the set) in parallel at the same time, the MM output is given by a combination of the estimates from these filters. Based on collected end-to-end delay data and preliminary data analysis, we propose an off-line model set design procedure using vector quantization (VQ) and short-term time series analysis so that MM methods can be applied to predict on-line measurement data. Numerical results show that the proposed MM predictor outperforms two widely used adaptive filters in terms of prediction accuracy and robustness.

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This paper deals with models of ballistic target (BT) motion during the boost phase for target tracking. Different options to improve the accuracy of modeling are discussed and several enhanced models are proposed. They include simple kinematic models of the so-called gravity turn (GT) target motion and more sophisticated models, accounting for the BT flight dynamics during boost, as well. Tracking simulations are presented.