Distributionally Robust Kalman Filtering over Finite and Infinite Horizon
<|reference_start|>Distributionally Robust Kalman Filtering over Finite and Infinite Horizon: This paper investigates the distributionally robust filtering of signals generated by state-space models driven by exogenous disturbances with noisy observations in finite and infinite horizon scenarios. The exact joint probability distribution of the disturbances and noise is unknown but assumed to reside within a Wasserstein-2 ambiguity ball centered around a given nominal distribution. We aim to derive a causal estimator that minimizes the worst-case mean squared estimation error among all possible distributions within this ambiguity set. We remove the iid restriction in prior works by permitting arbitrarily time-correlated disturbances and noises. In the finite horizon setting, we reduce this problem to a semi-definite program (SDP), with computational complexity scaling with the time horizon. For infinite horizon settings, we characterize the optimal estimator using Karush-Kuhn-Tucker (KKT) conditions. Although the optimal estimator lacks a rational form, i.e., a finite-dimensional state-space realization, it can be fully described by a finite-dimensional parameter. {Leveraging this parametrization, we propose efficient algorithms that compute the optimal estimator with arbitrary fidelity in the frequency domain.} Moreover, given any finite degree, we provide an efficient convex optimization algorithm that finds the finite-dimensional state-space estimator that best approximates the optimal non-rational filter in ${\cal H}_\infty$ norm. This facilitates the practical implementation of the infinite horizon filter without having to grapple with the ill-scaled SDP from finite time. Finally, numerical simulations demonstrate the effectiveness of our approach in practical scenarios.<|reference_end|>
@article{kargin2024distributionally,
    title={Distributionally Robust Kalman Filtering over Finite and Infinite
  Horizon},
    author={Taylan Kargin, Joudi Hajar, Vikrant Malik, Babak Hassibi},
    journal={arXiv preprint arXiv:2407.18837},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18837},
    primaryClass={math.OC cs.SY eess.SP eess.SY}
}