Robust State Estimation against Sparse Integrity Attacks

TL;DR

This paper introduces a robust state estimation method that withstands sparse integrity attacks on sensor measurements by using a convex optimization-based fusion approach, with proven robustness conditions and effectiveness demonstrated through simulations.

Contribution

It proposes a novel convex optimization framework for robust state estimation under sparse integrity attacks, with tight robustness conditions and bounds on attacker damage.

Findings

The estimator is robust under specified conditions.

Conditions for robustness are both necessary and sufficient.

Simulation results confirm the estimator's effectiveness.

Abstract

We consider the problem of robust state estimation in the presence of integrity attacks. There are $m$ sensors monitoring a dynamical process. Subject to the integrity attacks, $p$ out of $m$ measurements can be arbitrarily manipulated. The classical approach such as the MMSE estimation in the literature may not provide a reliable estimate under this so-called $(p,m)$-sparse attack. In this work, we propose a robust estimation framework where distributed local measurements are computed first and fused at the estimator based on a convex optimization problem. We show the sufficient and necessary conditions for robustness of the proposed estimator. The sufficient and necessary conditions are shown to be tight, with a trivial gap. We also present an upper bound on the damage an attacker can cause when the sufficient condition is satisfied. Simulation results are also given to illustrate the effectiveness of the estimator.