Abstract
Developing mathematical models for dynamic systems presents challenges in selecting state variables, crucial for minimizing computational operations during filter implementation. The paper addresses active identification of dynamic systems amid noise and measurement errors, using the Fisher information matrix. Additionally, it introduces Cholesky decomposition for generating correlated noise, demonstrating its applicability in practical problem-solving.
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