Data Fusion Using Robust Estimator for Uncertain Noisy Systems Over Sensor Networks.

This paper, verifies the problem of designing robust state estimator for multiple sensor networks with uncertain model and noisy measurements. Multi sensor data fusion by measurement fusion and state vector fusion structure using the Kalman filter were introduced. The standard Kalman filter requires an accurate system model. In order to get accurate information in modelling signals and sensors, the information form of robust Kalman filter by using the Krein space approach is proposed. Also, a new technique is presented by developing robust Kalman filter algorithm for fused state estimates. Simulation results demonstrate that the performance of data fusion with robust Kalman filter, as compared to data fusion with standard Kalman filter is improved.