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Estimation fusion for multisensor asynchronous sampling system is currently a hot subject of research. Under the assumption with independent and uncorrelated noises, some asynchronous fusion algorithms have been provided. Unfortunately, the general optimal solution with real-time update performance in linear minimum mean square error (LMMSE) hasn't been given up to now. Simultaneously, it is difficult...
Data fusion based on feed-forward natural network can get the goal of minimum error, as well as reduce redundant data to transmit and save energy consumption. BP neutral network is considered as one of the most mature algorithm, but the traditional BP algorithm converges slowly and easy traps in a local minimum value, so new improved BP algorithm named algebraic algorithm is put forward. The algebraic...
Influenced by the multihop transmission, the routing protocol and the dynamic topology of wireless sensor networks (WSN), the measurements sampled in order by the distributed sensors may arrive in the fusion center out of sequence when they are transported across WSN. In order to gain the optimal global performance, the fusion center should update the state estimate with these out-of-sequence measurements...
Taken as the whole networkpsilas information fusion, local sensor fusion, integrating signals from different sources and processing locally, is the first step work. Due to the output of sensor nodes is vulnerable to the impact of around environmental factors, such as temperature, humidity, noise, etc., and in order to solve nonlinear problems between input and output, a sensor output compensation...
Considering existing questions of the traditional synchronous fusion algorithms, e.g. assumptions of white noise and non-transmission delay produced by the influences of weather, humidity, magnetic field and communication line etc., the paper proposes an improved sequential weighted fusion algorithm, which cannot only deal with colored noise but also adapt automatically the case with network-induced...
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