Abstract
In this paper, the neural network (NN)-based navigation satellite subset selection is presented. The approach is based on approximation or classification of the satellite geometry dilution of precision (GDOP) factors utilizing the NN approach. Without matrix inversion required, the NN-based approach is capable of evaluating all subsets of satellites and hence reduces the computational burden. This would enable the use of a high-integrity navigation solution without the delay required for many matrix inversions. For overcoming the problem of slow learning in the BPNN, three other NNs that feature very fast learning speed, including the optimal interpolative (OI) Net, probabilistic neural network (PNN) and general regression neural network (GRNN), are employed. The network performance and computational expense on NN-based GDOP approximation and classification are explored. All the networks are able to provide sufficiently good accuracy, given enough time (for BPNN) or enough training data (for the other three networks).
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References
Chester M (1993) Neural networks: a tutorial. Prentice-Hall, Englewood Cliffs
Defigueiredo RJP (1983) A generalized Fock space framework for nonlinear system and signal analysis. IEEE Trans Circ Syst CAS-30:637–647
Defigueiredo RJP (1990) A new nonlinear functional analytic framework for modeling artificial neural networks. In: Proceedings of 1990 international symposium on circuits and systems, New Orleans, pp 723–726
Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, Englewood Cliffs
Jwo DJ, Chin KP (2002) Applying back-propagation neural networks to GDOP approximation. J Navig 54(2):97–108
Kihara M, Okada T (1984) A satellite selection method and accuracy for the global positioning system. Navig 31(1):8–20
Patra PK, Nayak M, Nayak SK, Gobbak NK (2002) Probabilistic neural network for pattern classification. In: Proceedings of 2002 international joint conference on neural networks, IJCNN ’02, pp 1200–1205
Simon D, El-Sherief H (1995a) Navigation satellite selection using neural networks. Neurocomputing 7:247–258
Simon D, El-Sherief H (1995b) Fault-tolerant training for optimal interpolative nets. IEEE Trans Neural Netw 6(3):1531–1535
Sin SK, defigueiredo RJP (1992) An evolution-oriented learning algorithm for the optimal interpolative net. IEEE Trans Neural Netw 3(2):315–323
Specht DF (1988) Probabilistic neural networks for classification, mapping, or associative memory. In: IEEE international conference on neural networks, pp 525–532
Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576
Stein BA (1985) Satellite selection criteria during Altimeter aiding of GPS. Navig J Inst Navig 32(2):149–157
Widrow B, Lehr MA (1990) 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proc IEEE 78(9):1415–1442
Yalagadda R, Ali I, Al-Dhahir N, Hershey J (2000) GPS GDOP metric. IEE Proc Radar Sonar Navig 147(5):259–264
Acknowledgments
This work has been supported in part by the National Science Council of the Republic of China through grant no. NSC 94-2212-E-019-003. Valuable suggestions and detailed comments by the reviewers are gratefully acknowledged.
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Jwo, DJ., Lai, CC. Neural network-based GPS GDOP approximation and classification. GPS Solut 11, 51–60 (2007). https://doi.org/10.1007/s10291-006-0030-z
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DOI: https://doi.org/10.1007/s10291-006-0030-z