An accurate estimation of the sensor systematic error is significant for improving the performance of target tracking system. The existing methods usually append the bias states directly to the variable states to form...An accurate estimation of the sensor systematic error is significant for improving the performance of target tracking system. The existing methods usually append the bias states directly to the variable states to form augmented state vectors and utilize the conventional Kalman estimator to achieve state vectors estimate. So doing is expensive in computation, and much work is devoted to decoupling variable states and systematic error. But the decentralied estimation of systematic errors and reduction of the amount of computation as well as decentralied track fusion are far from being realized. This paper addresses distributed track fusion problem in multi-sensor tracking system in the presence of sensor bias. By this method, variable states and systematic error is decoupled. Decentralized systematic error estimation and track fusion are achieved. Simulation results verify that this method can get accurate estimation of systematic error and state vector.展开更多
In order to estimate the systematic error in the processof maneuvering target adaptive tracking, a new method is proposed.The proposed method is a linear tracking scheme basedon a modified input estimation approach. A...In order to estimate the systematic error in the processof maneuvering target adaptive tracking, a new method is proposed.The proposed method is a linear tracking scheme basedon a modified input estimation approach. A special augmentationin the state space model is considered, in which both the systematicerror and the unknown input vector are attached to thestate vector. Then, an augmented state model and a measurementmodel are established in the case of systematic error, andthe corresponding filter formulas are also given. In the proposedscheme, the original state, the acceleration and the systematicerror vector can be estimated simultaneously. This method can notonly solve the maneuvering target adaptive tracking problem in thecase of systematic error, but also give the system error value inreal time. Simulation results show that the proposed tracking algorithmoperates in both the non-maneuvering and the maneuveringmodes, and the original state, the acceleration and the systematicerror vector can be estimated simultaneously.展开更多
基金This work was supported by the Special Fund of Excellent Doctor'Degree Dissertation in China and the Basic Research Fund of National University of Defense Technology in China.
文摘An accurate estimation of the sensor systematic error is significant for improving the performance of target tracking system. The existing methods usually append the bias states directly to the variable states to form augmented state vectors and utilize the conventional Kalman estimator to achieve state vectors estimate. So doing is expensive in computation, and much work is devoted to decoupling variable states and systematic error. But the decentralied estimation of systematic errors and reduction of the amount of computation as well as decentralied track fusion are far from being realized. This paper addresses distributed track fusion problem in multi-sensor tracking system in the presence of sensor bias. By this method, variable states and systematic error is decoupled. Decentralized systematic error estimation and track fusion are achieved. Simulation results verify that this method can get accurate estimation of systematic error and state vector.
基金supported by the National Natural Science Foundation of China(91538201)
文摘In order to estimate the systematic error in the processof maneuvering target adaptive tracking, a new method is proposed.The proposed method is a linear tracking scheme basedon a modified input estimation approach. A special augmentationin the state space model is considered, in which both the systematicerror and the unknown input vector are attached to thestate vector. Then, an augmented state model and a measurementmodel are established in the case of systematic error, andthe corresponding filter formulas are also given. In the proposedscheme, the original state, the acceleration and the systematicerror vector can be estimated simultaneously. This method can notonly solve the maneuvering target adaptive tracking problem in thecase of systematic error, but also give the system error value inreal time. Simulation results show that the proposed tracking algorithmoperates in both the non-maneuvering and the maneuveringmodes, and the original state, the acceleration and the systematicerror vector can be estimated simultaneously.
