Abstract
Stack sequential decoding algorithms of information theory are used to estimate the states of multidimensional dynamic systems with interference. Each component of the observation model is a non-linear function of only one state component, arbitrary random interference, and observation noise. The state model is a non-linear function of states and disturbance noise. States are estimated, component-by-component, in parallel. This results in memory reduction for the implementation of state estimation. The use of a stack sequential decoding algorithm makes state estimation faster, compared to estimation with the Viterbi decoding algorithm. Simulation results have shown that the proposed estimation scheme performs well, whereas the classical estimation schemes cannot, in general, be used to estimate the states of dynamic models with arbitrary random interference.
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