Work

Distributionally Robust Kalman Filtering With Volatility Uncertainty

Year: 2024

Type: article

Abstract: This work presents a distributionally robust Kalman filter to address uncertainties in noise covariance matrices and predicted covariance estimates. We adopt a distributionally robust formulation using bicausal optimal transport to characterize a set of plausible alternative models. The optimization problem is transformed into a convex nonlinear semi-definite programming problem and solved using the trust-region interior point method with the aid of$LDL^\top$decomposition. The empirical outperformance is demonstrated through target tracking and pairs trading.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2024.3522192

Author: Bingyan Han

Institution: The Hong Kong University of Science and Technology (Guangzhou)

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: University of Michigan, National Natural Science Foundation of China, Hong Kong University of Science and Technology

Awards: G2460922350, G669497379, G887646106