Recently, lots of smoothing techniques have been presented for maneuvering target tracking. Interacting multiple model-probabilistic data association （IMM-PDA） fixed-lag smoothing algorithm provides an efficient sol...Recently, lots of smoothing techniques have been presented for maneuvering target tracking. Interacting multiple model-probabilistic data association （IMM-PDA） fixed-lag smoothing algorithm provides an efficient solution to track a maneuvering target in a cluttered environment. Whereas, the smoothing lag of each model in a model set is a fixed constant in traditional algorithms. A new approach is developed in this paper. Although this method is still based on IMM-PDA approach to a state augmented system, it adopts different smoothing lag according to diverse degrees of complexity of each model. As a result, the application is more flexible and the computational load is reduced greatly. Some simulations were conducted to track a highly maneuvering target in a cluttered environment using two sensors. The results illustrate the superiority of the proposed algorithm over comparative schemes, both in accuracy of track estimation and the computational load.展开更多
In this paper, a new approach to H-infinity fixed-lag smoothing is developed by applying the innovation analysis theory. The smoother is derived by resorting to the augmentation state. However, being completely differ...In this paper, a new approach to H-infinity fixed-lag smoothing is developed by applying the innovation analysis theory. The smoother is derived by resorting to the augmentation state. However, being completely different from the previous work,the augmented state here is considered as just a theoretical mathematical tool for deriving the estimator. A distributed algorithm for the Riccati equation of the augmented system is presented. The calcuhtion of the estimator does not require any augmentation. The comparison of the computation costs between the new approach and previous work is made. The main technique applied in this paper is the re-organized innovation analysis in an indefinite space.展开更多
基金This work is supported by the Projects of the State Key Fundamental Research (No. 2001CB309403)
文摘Recently, lots of smoothing techniques have been presented for maneuvering target tracking. Interacting multiple model-probabilistic data association （IMM-PDA） fixed-lag smoothing algorithm provides an efficient solution to track a maneuvering target in a cluttered environment. Whereas, the smoothing lag of each model in a model set is a fixed constant in traditional algorithms. A new approach is developed in this paper. Although this method is still based on IMM-PDA approach to a state augmented system, it adopts different smoothing lag according to diverse degrees of complexity of each model. As a result, the application is more flexible and the computational load is reduced greatly. Some simulations were conducted to track a highly maneuvering target in a cluttered environment using two sensors. The results illustrate the superiority of the proposed algorithm over comparative schemes, both in accuracy of track estimation and the computational load.
基金The work ofthe first author was supported bythe National Nature Science Foundation of China (No .60174017,60574016) .
文摘In this paper, a new approach to H-infinity fixed-lag smoothing is developed by applying the innovation analysis theory. The smoother is derived by resorting to the augmentation state. However, being completely different from the previous work,the augmented state here is considered as just a theoretical mathematical tool for deriving the estimator. A distributed algorithm for the Riccati equation of the augmented system is presented. The calcuhtion of the estimator does not require any augmentation. The comparison of the computation costs between the new approach and previous work is made. The main technique applied in this paper is the re-organized innovation analysis in an indefinite space.
