Uncertain IMM Estimator for Multi-Sensor Target Tracking

Interacting Multiple Model (IMM) estimator can provide better performance of target tracking than mono model Kalman filter. In multi-sensor system ordinarily, availability of measurement from different sensors is stochastic, and it is difficult to construct uniform global observation vector and observation matrix appropriately in existing method. An IMM estimator for uncertain measurement is presented. By the method invalid measurement is regarded as outlier, and approximation is reconstructed by feedback of system state estimation of fusion center. Then nominally generalized certain measurement can be obtained by substituting reconstructed one for invalid one. The generalized certain measurement can be centralized to construct global measurement and provided to IMM estimator, and existing multi-sensor IMM estimation method is generalized to uncertain environment. Theoretical analysis and simulation results show the effectiveness of the method.