Filtering for stochastic uncertain systems with non-logarithmic sensor resolution

The sensor resolution is a most basic parameter for nearly all kinds of sensors which is so important that cannot be ignored for any signal processing problems. In this paper, the robust filtering problem is investigated for a class of stochastic systems with model uncertainty and non-logarithmic sensor resolution. A novel soft measurement model (SMM) is proposed. It has advantages of zero mean sensor resolution-induced uncertainty (SRU) and maximum signal resolution ratio (SRR). Based on the proposed model, a new robust filter (RF) is put forward which takes both model uncertainty and sensor resolution into full consideration. By designing the filter gain appropriately, the upper bound of estimation error covariance is obtained and minimized at each time step. The corresponding filtering algorithm is recursive, thus suitable for real-time online applications. Finally, a simulation study is carried out to demonstrate the effectiveness and applicability of our proposed SMM and RF.