Multi-sensor optimal information fusion Kalman filter

Automatica ◽  
2004 ◽  
Vol 40 (6) ◽  
pp. 1017-1023 ◽  
Author(s):  
Shu-Li Sun ◽  
Zi-Li Deng
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Optimal Information

Multi-Sensor Information Fusion Kalman Filter Weighted by Scalars for Systems with Colored Measurement Noises

2005 ◽  
Vol 127 (4) ◽  
pp. 663-667 ◽  
Author(s):  
Shu-Li Sun ◽  
Zi-Li Deng
Keyword(s):  
Kalman Filter ◽  
Control Systems ◽  
Information Fusion ◽  
Fault Tolerant ◽  
Minimum Variance ◽  
Multiple Sensors ◽  
Kalman Predictor ◽  
Optimal Information ◽  
Measurement Noises ◽  
Correlated Noises

An optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense. Based on this fusion criterion, a scalar weighting information fusion decentralized Kalman filter is given for discrete time-varying linear stochastic control systems measured by multiple sensors with colored measurement noises, which is equivalent to an information fusion Kalman predictor for systems with correlated noises. It has a two-layer fusion structure with fault tolerant and robust properties. Its precision is higher than that of each local filter. Compared with the fusion filter weighted by matrices and the centralized filter, it has lower precision when all sensors are faultless, but has reduced computational burden. Simulation researches show the effectiveness.

An Information Fusion Algorithm Based on Kalman Filtering

2013 ◽  
Vol 444-445 ◽  
pp. 1072-1076
Author(s):  
Xiu Hu Tan
Keyword(s):  
Mathematical Model ◽  
Information Fusion ◽  
Minimum Mean Square Error ◽  
Minimum Variance ◽  
Probability Method ◽  
Fusion Algorithm ◽  
Multisensor Systems ◽  
Optimal Information ◽  
Noise Statistics ◽  
The Mathematical Model

For the multisensor systems with unknown noise variances, by the statistics method, the mathematical model and the noise statistics are essential, and this limitation was settled by adaptive algorithm. The adaptive Kalman filter was proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. Based on the probability method and the scalar weighting optimal information fusion criterion in the minimum variance sense, the algorithm can not only optimize the multi-channel data, but also obtain the minimum mean square error (MMSE) by introducing fusion equation, namely the algorithm is optimal under the sense of MMSE, and the error is the least than the original Kalman information fusion algorithm. The test result shows that the algorithm can precede information fusion effectively under the distributed acquisition system.

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