Abstract
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 appropri-ately 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 estima-tion of fusion center. Then nominally generalized certain measurement can be obtained by substituting re-constructed 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 general-ized to uncertain environment. Theoretical analysis and simulation results show the effectiveness of the method.
Highlights
In many applications of multi-sensor system, such as target tracking and surveillance, intelligent robot or wireless sensor networks (WSN), a group of heterogeneous sensors with different function and performance are integrated to operate cooperatively
Because mono model tracking method can not adapt to the target maneuver correctly, many multiple model approaches are available for target tracking and Interacting Multiple Model (IMM) method is used widely [6,7,8]
When invalid measurement exists in multi-sensor system, if certain method is applied to construct global measurement by (4) and (5), because availability of measurement is not considered in certain method and all measurement data are regarded as valid ones, the global observation vector Zi(k) is incorrect and filters of IMM estimator can perform incorrectly
Summary
In many applications of multi-sensor system, such as target tracking and surveillance, intelligent robot or wireless sensor networks (WSN), a group of heterogeneous sensors with different function and performance are integrated to operate cooperatively. To estimate the state such as location, size, pose and motion information of maneuvering target accurately and reliably, multi-sensor fusion is necessary to exploit the advantages of each sensor [1,2,3]. In IMM estimator, original measurement information, including measurement data and characteristic of precision, is centralized to construct global measurement, and filtered with multiple models and combined to obtain uniform optimal target state estimate. It is assumed in existing IMM algorithm that observation is available and covariance of measurement is known already. In the method invalid measurement is regarded as outlier, and generalized measurement is reconstructed based on target state feedback of IMM estimator
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