Particle filtering has a great potential for solving highly nonlinear and non-Gaussian estimation problems, generally intractable within a standard linear Kalman filtering based framework. However; the implementation of particle filters (PFs) is rather computationally involved, which nowadaysprevents them frompractical real-world application. A natural idea to make PFs feasible for "real-time" data processing is IO implement them on distributed multiprocessor computer systems. This paper presents three schemes for distributing the computations of generic particle filters, including resampling and. optionally, a Metropolis-Hastings (MH) step. Simulation results based on a maneuvering target tracking scenario show that distributed implementations can provide a promising solution 10 the steep computational burden incurred when using a large number of particles.

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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