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Neural network aided adaptive Kalman filter for GPS/INS navigation system design

Jwo Dah-Jing, Jyh-Jeng Chen
A mechanism called PSO-RBFN, which is composed of radial basis function (RBF) network and particle swarm optimization (PSO), for predicting the errors and to filtering the high frequency noise is proposed. As a model nonlinearity identification mechanism, the PSO-RBFN will implement the on-line identification of nonlinear dynamics errors such that the modeling error can be compensated. The PSO-RBFN will be applied to the loosely-coupled Global Positioning System (GPS)/inertial navigation systems (INS) navigation filter design and has demonstrated substantial performance improvement in comparison with the standard Kalman filtering method.
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