GPS/INS navigation filter designs using neural network with optimization techniques

Abstract

The Global Positioning System (GPS) and inertial navigation systems (INS) have complementary operational characteristics and the synergy of both systems has been widely explored. Most of the present navigation sensor integration techniques are based on Kalman filtering estimation procedures. For obtaining optimal (minimum mean square error) estimate, the designers are required to have exact knowledge on both dynamic process and measurement models. In this paper, a mechanism called PSO-RBFN, which combines Radial Basis Function (RBF) Network andParticle 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 is applied to the loosely-coupled GPS/INS navigation filter design and has demonstrated substantial performance improvement in comparison with the standard Kalman filtering method.