Distributed State Estimation for Heterogeneous Mobile Sensor Networks with Sensor Faults

Consensus-based distributed state estimation for homogeneous mobile sensor networks has been widely studied currently. However, the sensors used for target detection and tracking in practical applications usually have different physical properties, different measuring range or accuracy. In order to realize data exchange between sensors which are of heterogeneity and to obtain a consistent and precise target state estimation result, an improved Cubature Kalman Filtering (CKF) method based on weighted average consensus theory is put forward herein. Moreover, the adverse impacts caused by sensor faults have been taken into consideration in modeling. The stochastic boundedness of the estimation errors for the discussed sensor networks is proved in the paper. Finally, numerical simulations are provided to demonstrate the validity of the proposed method.