Least-Squares Linear Estimation for Multirate Uncertain Systems subject to DoS Attacks
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-...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:ruja.ujaen.es:10953/6757 |
| Acceso en línea: | https://doi.org/10.53941/ijndi.2025.100014 https://www.sciltp.com/journals/ijndi/articles/2506000865 https://hdl.handle.net/10953/6757 |
| Access Level: | acceso abierto |
| Palabra clave: | multirate systems least-squares estimation random parameter matrices DoS attacks compensation strategies hold-input prediction compensation Matemáticas |
| Sumario: | 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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