Convex Reformulation of Information Constrained Linear State Estimation with Mixed-Binary Variables for Outlier Accommodation

Hu, Wang; Jiang, Zeyi; Mohsenian-Rad, Hamed; Farrell, Jay A.; University of California, Riverside. Dept. of Electrical and Computer Engineering

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Convex Reformulation of Information Constrained Linear State Estimation with Mixed-Binary Variables for Outlier Accommodation

2024-09-13
By Hu, Wang ; Jiang, Zeyi ; Mohsenian-Rad, Hamed ; ...

[PDF-285.41 KB]

English

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Creators:

Hu, Wang ; Jiang, Zeyi ; Mohsenian-Rad, Hamed ; Farrell, Jay A.

Hu, Wang ; Jiang, Zeyi ; Mohsenian-Rad, Hamed ; Farrell, Jay A. Less -
Corporate Creators:

University of California, Riverside. Dept. of Electrical and Computer Engineering
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Control Estimation Theory Linear Systems Systems Tools
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Journal Article
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Open Access; arXiv- Non-exclusive license to distribute
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United States
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arXiv [Cornell University]
Abstract:

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 is 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.
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This is an open access article under the terms of arXiv.org - Non-exclusive license to distribute.
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US Transportation Collection
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urn:sha-512:284eecc59d42fe2454500b45a282cc3665b9739473bf2a9be466f725ca5489898a93e87cf33812a0adcb969d9f521e95a8ba34f79e964dd9709e335fd1e561a2
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Convex Reformulation of Information Constrained Linear State Estimation with Mixed-Binary Variables for Outlier Accommodation

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