Convex Reformulation of Information Constrained Linear State Estimation with Mixed-Binary Variables for Outlier Accommodation
<|reference_start|>Convex Reformulation of Information Constrained Linear State Estimation with Mixed-Binary Variables for Outlier Accommodation: This article considers the challenge of accommodating outlier measurements in state estimation. The Risk-Averse Performance-Specified (RAPS) state estimation approach addresses outliers as a measurement selection Bayesian risk minimization problem subject to an information accuracy constraint, which is a non-convex optimization problem. Prior explorations into RAPS rely on exhaustive search, which becomes computationally infeasible as the number of measurements increases. This paper derives a convex formulation for the RAPS optimization problems via transforming the mixed-binary variables into linear constraints. The convex reformulation herein can be solved by convex programming toolboxes, significantly enhancing computational efficiency. We explore two specifications: Full-RAPS, utilizing the full information matrix, and Diag-RAPS, focusing on diagonal elements only. The simulation comparison demonstrates that Diag-RAPS is faster and more efficient than Full-RAPS. In comparison with Kalman Filter (KF) and Threshold Decisions (TD), Diag-RAPS consistently achieves the lowest risk, while achieving the performance specification when it is feasible.<|reference_end|>
@article{hu2024convex,
    title={Convex Reformulation of Information Constrained Linear State Estimation
  with Mixed-Binary Variables for Outlier Accommodation},
    author={Wang Hu, Zeyi Jiang, Hamed Mohsenian-Rad, and Jay A. Farrell},
    journal={arXiv preprint arXiv:2409.08626},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08626},
    primaryClass={eess.SY cs.SY}
}