A detection-estimation approach to filtering for Gaussian systems with intermittent observations

Abstract

In this paper we consider the problem of state estimation for linear discrete-time Gaussian systems with intermittent observations. Intermittent observations result from packet dropouts when data travel along unreliable communication channels, as in the case of wireless sensor networks, or networked control systems. We assume that the receiver does not know the sequence of packet dropouts, which is a common situation, e.g., in wireless sensor networks or in networks that cannot rely on protocols that provide information on packet loss. In this paper we propose a detection-estimation approach to the problem of state estimation. The estimator consists of two stages: the first is a nonlinear optimal detector, which decides if a packet dropout has occurred, and the second is a time-varying Kalman filter, which is fed with both the observations and the decisions from the first stage. The overall estimator has finite memory and the tradeoff between performance and computational complexity can be easily controlled. Simulation results highlight the effectiveness of the proposed approach, which outperforms the linear optimal filter of Nahi. Finally, the method is amenable to generalization.