Sequential Bayesian Estimation With Censored Data for Multi-Sensor Systems

In this paper, a new framework for sequential Bayesian estimation in sensor networks is proposed, which consists of two processes: censoring of measurements at local sensors and fusion of both received measurements and missing ones at the fusion center (FC). In our scheme, each local sensor maintains a Kalman filter (KF) for a linear Gaussian system or an extended Kalman filter (EKF) for a nonlinear system and the FC runs a particle filter (PF) to track the system state. Informative measurements are selected for transmission by an innovation based per-sensor censoring process executed at the sensors at each time. Though the less informative measurements are not sent to the FC, their absence still conveys some information, and the proposed scheme exploits such information from the missing messages. Numerical results show that, under the same bandwidth constraint, the proposed scheme outperforms the one that ignores missing data information and the one that selects sensors randomly for information transmission.