Cyber-Physical Systems (CPSs) represent a critical class of interconnected systems where physical processes are tightly integrated with computation and communication layers. Their growing deployment in safety-critical infrastructures, such as autonomous transportation, industrial automation, and smart energy grids, makes them highly exposed to malicious intrusions. Among the most harmful threats are adversarial sensor attacks, which can stealthily corrupt measurement data and undermine the reliability of state estimation, control performance, and safety. Traditional observer-based approaches, such as the Luenberger observer, provide accurate state estimation under nominal conditions but lack robustness against adversarial corruption. In this context, sparsity-aware estimation techniques inspired by compressed sensing and convex optimization have recently emerged as promising tools.