GPS/INS Navigation Filter Designs Using Neural Network with Optimization Techniques

  • Dah-Jing Jwo
  • Jyh-Jeng Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


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.


Global Position System Particle Swarm Optimization Radial Basis Function Kalman Filter Particle Swarm Optimization Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dah-Jing Jwo
    • 1
  • Jyh-Jeng Chen
    • 1
  1. 1.Department of Communications and Communications EngineeringNational Taiwan Ocean UniversityKeelungChina

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