A novel algorithm for SINS/CNS/GPS integrated navigation system

In this paper we present a novel algorithm for SINS(Strapdown Inertial Navigation)/CNS(Celestial Navigation System)/GPS(Global Positioning System) integrated navigation system. This novel algorithm is called as federated unscented particle filter(FUPF), and the SINS/CNS/GPS system models are nonlinear and non-Gaussian. This algorithm uses the UKF(Uscented Kalman Filter) to estimate the local filters, and the estimate results are employed to generate the importance proposal distributions of local filters. Then, the output of every local filter can be estimated from the importance proposal distributions by particle filter. Finally, this algorithm uses the federated filter method to fuse together the outputs from local UPF(Uscented Particle Filter) filters, and the final total estimation of the SINS/CNS/GPS system can be obtained. In this algorithm the particle filter incorporates the latest observations of local filters into a prior updating routine. In addition, the algorithm is not restricted by assumptions of linearity or Gaussian noise: it may be applied to any state transition or measurement model. Specifically, we apply the algorithm to maneuvering vehicles and simulation results show that the algorithm is more accurate than the federated UKF algorithm in the nonlinear and non-Gaussian models.

[1]  N. A. Carlson.  Federated square root filter for decentralized parallel processors , 1990 .

[2]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[3]  Eric Moulines,et al.  Comparison of resampling schemes for particle filtering , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[4]  N. A. Carlson,et al.  Federated filter for fault-tolerant integrated navigation systems , 1988, IEEE PLANS '88.,Position Location and Navigation Symposium, Record. 'Navigation into the 21st Century'..

[5]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[6]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[7]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[8]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[9]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[10]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..