Quanbo Ge

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Kalman filter is a powerful tool in target tracking and self-localization across wireless sensor networks with many constraints. This paper considers the filters design for networked systems with combined constraints of bandwidth and random delay, and proposes a kind of universal networked Kalman estimator for given linear time invariant (LTI) or(More)
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(More)
Multisensor data fusion based on wireless sensor networks (WSN) has many advantages over the traditional data fusion and is faced with many challenges. Because sensor nodes in WSN decentralize over wide area and data must be transmitted to the central processor by use of special wireless transport protocols for fusion, data delay and missing continually(More)
Motivated by the extensive application of sensor networks in the multisensor target tracking systems, the problem of data fusion with mixed time delays which includes short timedelay and long time-delay is considered in this paper. In order to overcome several primary problems occurred in the existing fusion methods based on the "Out-Of-Sequence"(More)
Consider the decentralized estimation problem of dynamic stochastic process in a sensor network. Due to bandwidth constraints, only quantized messages of the original information from local sensor are available. For a class of vector state-vector observation model, an adaptive quantization strategy and sequential filter technique are introduced to design(More)
It is well known that estimation performance of the Kalman filtering (KF) depends closely on systemic observability. Moreover, observable degree is usually used to measure the ability of observability on systemic state variables in control and estimation systems. Thereby, there should be a corresponding relation between the estimation performance of the KF(More)