Communication jamming across various durations and frequencies leads to unpredictable and time-varying measurement transmission intervals in networked systems. Consequently, the discrete-time models of filtering error systems become time-dependent, thereby complicating the design of filters. This paper addresses the ongoing challenge of \({\mathcalligra {H}}_\infty\) filtering for networked sampled-data systems subjected to communication jamming attacks. By incorporating the uncertain model within a polytopic framework, the analysis and synthesis conditions for a full-order \({\mathcalligra {H}}_\infty\) filter are expressed as linear matrix inequalities. The proposed filter guarantees both asymptotic stability and \({\mathcalligra {H}}_\infty\) performance under stealthy jamming attacks. A key novelty of the proposed approach is that it operates without requiring prior knowledge of the jammer’s active or inactive periods. Another advantage of the method lies in its capability to tune the desired level of accuracy by increasing the number of vertices in the polytopic model, albeit at the cost of higher computational demand. Three benchmark examples are presented to demonstrate the effectiveness of the proposed strategy and to compare its performance with existing filters. The proposed filter achieves over 80% improvement in \({\mathcalligra {H}}_\infty\) performance compared to existing methods. Its offline design and enhanced performance make it particularly suitable for networked and embedded control systems with constrained computational or communication capabilities.
