Transactions of Nanjing University of Aeronautics & Astronautics
COMBINATION OF DISTRIBUTED KALMAN FILTER AND BP NEURAL NETWORK FOR ESG BIAS MODELIDEN TIFICATION
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Abstract:
By combining the distributed Kalman filter (DKF) with the back propagation neural network (BPNN), a novel method is proposed to identify the bias of electrostatic suspended gyroscope (ESG). Firstl y, the data sets of multimeasurements of the same ESG in different noise env ironments are ＂mapped＂ into a sensor network, and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preproc essed results into the trained input and the desired output of neural network, B PNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effect ive for the model identification of the ESG bias.
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