Abstract. The extension of the adaptive estimation theory for nonlinear correlation-extremum systems with random structure to the case when the measurements noise model is a spatial-timevarying Gaussian-Markov colored process is presented and the new signal processing algorithms are derived which provide the system operation in varying and uncertain external conditions.
The correlation-extremum systems theory is extended to stochastic systems with random structure or with switching parameters and the new suboptimal (due to the nonlinear system state and measurement equations) filtering and parameter identification algorithms and their linearized form are derived, which provide adaptive features and reliable operation for the proposed combined correlation-extremum dynamic systems with random structure under environment influences, and represent new solutions of the linearization problem for the case of great estimation errors. The obtained linearized solution allows the simplification of the filter a priori performance investigation at the signal processing system design stage.
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