Abstract
•The recursive filtering problem is studied for stochastic parameter systems.•Quantization effects and packet disorders are considered.•Desired upper bounds on the resultant filtering error covariances are guaranteed.•The obtained upper bounds are minimized by acquiring appropriate filter gains.•Sufficient conditions are established to ensure the mean-square boundedness of filtering errors.
In this paper, the recursive filtering problem is put forward for stochastic parameter systems subject to quantization effects and packet disorders. Before entering communication networks, measurement outputs are quantized by logarithmic quantizers. The packet disorders result from transmission delays which are provoked by communication constraints and occur randomly in the sensor-to-filter channel. In case of measurement quantizations and packet disorders, the objective of this paper is to devise a novel recursive filter approach that is capable of 1) guaranteeing desired upper bounds on the resultant filtering error covariances; and 2) minimizing such upper bounds by acquiring appropriate filter gains. Furthermore, sufficient conditions are established to ensure the mean-square boundedness of filtering errors by means of stochastic analysis techniques. At last, simulations are given to validate the applicability of our designed approach.