Robust State Space Filtering under Incremental Model Perturbations Subject to a Relative Entropy Tolerance

• Main Authors: Levy, Bernard C, Nikoukhah, Ramine
• Format: Journal Article
• Language: English
• Published: 14.04.2010
• Subjects: Computer Science - Information Theory; Computer Science - Systems and Control; Mathematics - Information Theory; Mathematics - Optimization and Control
• DOI: 10.48550/arxiv.1004.2519

This paper considers robust filtering for a nominal Gaussian state-space model, when a relative entropy tolerance is applied to each time increment of a dynamical model. The problem is formulated as a dynamic minimax game where the maximizer adopts a myopic strategy. This game is shown to admit a saddle point whose structure is characterized by applying and extending results presented earlier in [1] for static least-squares estimation. The resulting minimax filter takes the form of a risk-sensitive filter with a time varying risk sensitivity parameter, which depends on the tolerance bound applied to the model dynamics and observations at the corresponding time index. The least-favorable model is constructed and used to evaluate the performance of alternative filters. Simulations comparing the proposed risk-sensitive filter to a standard Kalman filter show a significant performance advantage when applied to the least-favorable model, and only a small performance loss for the nominal model.