Abstract

An objective for future wireless communication systems is to increase capacity and transmission quality by exploiting the properties of the radio (propagation) channel. An important issue is to improve the knowledge of the temporal behavior of the radio channel through tracking and estimation algorithms. In this contribution the variation of the radio channel is described using a state-space model, where the state space consists of azimuth of arrival, delay, Doppler frequency, complex amplitude and the parameters' rate of change of propagation paths. Two nonlinear filtering algorithms are compared, i.e. a particle filter and an extended kalman filter. Simulations are conducted to evaluate the performance of these algorithms in a single-scatterer environment. Experimental investigation using measurement data is presented.

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