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State estimation for systems with unknown state transfer matrix and multiplicative noise is very classical and important in signal processing and target tracking. Aiming at the problem, an adaptive estimation algorithm which can deal with unknown state transfer matrix and multiplicative noise has been presented by Dongsheng Chu for linear systems under without considering multiplicative noise in [1]...
Performance of the Kalman filter (KF) is degraded when dealing with nonlinear dynamic systems. For a kind of nonlinear biomechatronics system, a fifth-degree ensemble iterated cubature square-root information filter (EsFICIF), which can effectively improve estimation performance, is proposed by combing many estimation schemes. Moreover, the associated multisensor fusion is deeply studied based on...
Observability and observable degree, which are derived from modern control theory and relative to control and estimation performances, are both important concepts for state estimation. For linear time-invariant systems, judgement of observability and evaluation of observable degree are irrelevant to filtering process and parameters. Thereby, it has limited application ability. Different from linear...
Nonlinear filtering is one of hot and difficult topics in the target tracking. The classical Extended Kalman Filter (EKF) has some shortages such as tracking accuracy and stability because of the using of linearization. So, the Unscented Kalman Filter (UKF) based on the nonlinear transformation and sampling has been widely applied. In this paper, we devote to research the nonlinear Unscented Kalman...
For the nonlinear multisensor dynamic system, a new nonlinear data fusion algorithm is put forward based on filter step by step and strong tracking filter (STF) in the case of that there exist in mild transmission delay and the delay can happen while the measure data is propagated by local network from each local observation site to central processor. Suppose that the estimate and its covariance is...
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