Title :
Multi-sensor Information Fusion Method Based on the Neural Network Algorithm
Author_Institution :
Hunan Railway Prof. Technol. Coll., Zhuzhou, China
Abstract :
In order to improve the detection character of target parameter with multi-sensor, we proposed a multi-sensor information fusion approach based on neural network algorithm with orthogonal basis functions and recursive least square algorithm. The detection data of the multi-sensor is processed using neural network approach based on recursive least square algorithm, and the average of the neural network outputs is used to implement multi-sensor information fusion. To validate the validity of the algorithm, the simulating example of the multi-sensor information fusion was given. The result shows that the information fusion approach of multi-sensor using orthogonal basis neural network based on the recursive least square algorithm is effective.
Keywords :
least squares approximations; neural nets; recursive estimation; sensor fusion; character detection; data detection; multisensor information fusion method; neural network algorithm; orthogonal basis functions; recursive least square algorithm; target parameter; Computer networks; Defense industry; Electrical equipment industry; Fuzzy logic; Industrial control; Least squares methods; Logic testing; Neural networks; Process control; Vectors; information fusion; multi-sensor; neural network;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.530