Recursive Estimation With Compensation Strategies Under Random Access Protocol and Deception Attacks

In this paper, recursive least-squares linear estimation algorithms are proposed for stochastic systems influenced by uniform quantization, random access protocol (RAP) and deception attacks. With the purpose of enhancing communication efficiency and reducing unnecessary data collisions, RAP is adopted to schedule data signal transmissions that are also subject to deception attacks. In order to alleviate the side effect of missing information caused by RAP, three compensation strategies (zero-input, zero-order hold and prediction-compensation) are utilized. By resorting to an innovation method, covariance-based filters are designed and then fixed-point smoothers are obtained in light of available observations. Finally, a simulation experiment with comparisons is employed to demonstrate the effectiveness of the developed recursive estimation schemes, where the influence of attack probabilities on estimation accuracy is evaluated.