Least-Squares Linear Estimation for Multirate Uncertain Systems subject to DoS Attacks
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Abstract
This paper investigates the least-squares linear estimation problem for multirate systems with stochastic parameter matrices, under the influence of random denial-of-service (DoS) attacks. These attacks can severely impair the performance of estimation algorithms by causing intermittent loss of mea- surement data. To counteract the adverse effect of DoS attacks, two compensation strategies –hold-input and prediction compensation– are used. For each of these strategies, specific recursive filtering and smoothing algorithms are designed. A key advantage of the proposed methodology is its ability to oper- ate without requiring a detailed signal evolution model, relying only on the mean and covariance func- tions of the involved processes. The effectiveness of the proposed approaches is validated through numerical simulations, which highlight how common network-induced phenomena, such as missing observations, can be incorporated into the framework of systems with random parameter matrices and, additionally, they provide insights into estimation performance under different attack probabilities.
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