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 stoc...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.展开更多
To improve the tracking accuracy and stability of an optic-electronic target tracking system,the concept of generalized synergic target and an algorithm named error-space estimate method is presented.In this algorithm...To improve the tracking accuracy and stability of an optic-electronic target tracking system,the concept of generalized synergic target and an algorithm named error-space estimate method is presented.In this algorithm,the motion of target is described by guide data and guide errors,and then the maneuver of the target is separated into guide data and guide errors to reduce the maneuver level.Then state estimate is implemented in target state-space and error-space respectively,and the prediction data of target position are acquired by synthesizing the filtering data from target state-space according to kinematic model and the prediction data from errorspace according to guide error model.Differing from typical multi-model method,the kinematic and guide error models work concurrently rather than switch between models.Experiment results show that the performance of the algorithm is better than Kalman filter and strong tracking filter at the same maneuver level.展开更多
文摘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.
基金supported by the Hi-Tech Research and Development Program of China.
文摘To improve the tracking accuracy and stability of an optic-electronic target tracking system,the concept of generalized synergic target and an algorithm named error-space estimate method is presented.In this algorithm,the motion of target is described by guide data and guide errors,and then the maneuver of the target is separated into guide data and guide errors to reduce the maneuver level.Then state estimate is implemented in target state-space and error-space respectively,and the prediction data of target position are acquired by synthesizing the filtering data from target state-space according to kinematic model and the prediction data from errorspace according to guide error model.Differing from typical multi-model method,the kinematic and guide error models work concurrently rather than switch between models.Experiment results show that the performance of the algorithm is better than Kalman filter and strong tracking filter at the same maneuver level.
