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Wavelet Neural Network for Corrections Prediction in Single-Frequency GPS Users

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Accurate and reliable position determination is a vital component in Global Positioning System (GPS). GPS positioning errors occur from the cumulative effects of receiver, satellite and atmosphere, and also due to the U.S. military intentionally such as Selective Availability (SA). In order to improve the accuracy of positions provided by GPS additional correction information may be used, such as Differential GPS (DGPS) or other sensors to enhance position reliability. The DGPS has the problem of slow updates. To overcome this limitation, DGPS corrections prediction has been proposed. The ability of Neural Networks (NNs) to discover nonlinear relationships in input data makes them ideal for modeling nonlinear dynamic systems. The Wavelet Neural Network (WNN) employing nonlinear wavelet basis function, which are localized in both the time and frequency space, has been developed as an alternative approach to nonlinear fitting problem. Particle Swarm Optimization (PSO), a global optimization method, is used to train the WNN. In this paper, a WNN trained by a PSO algorithm is proposed for DGPS corrections prediction in single-frequency GPS receivers. Experimental results show the feasibility and effectiveness of the proposed method. The results are analyzed and compared with WNN trained by Back Propagation (BP) algorithm. The experimental results show that WNN, trained by the PSO algorithm, is able to reduce RMS errors to less than 1 m with SA on and 0.6 m with SA off.

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References

  1. Parkinson BW, Spilker JJ, Jr, Axelra P, Enge P (1996) Global positioning system: theory and applications. The American Institute of Aeronautics and Astronautics, Washington

    Google Scholar 

  2. McDonald KD (2002) Modernization of GPS: plans, new capabilities, and the future relationship to Galileo. J Global Position Syst 1(1): 1–17

    Article  Google Scholar 

  3. Kobayashi K, Cheok KC, Watanabe K, Munekata F (1998) Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique. IEEE Trans Ind Electron 45(3): 510–518

    Article  Google Scholar 

  4. Jain AK, Mao J, Mohiuddin K (1996) Artificial neural networks: a tutorial. IEEE Trans Comput 29(3): 31–44

    Google Scholar 

  5. Chen Z, Feng TJ, Meng QC (1999) The application of wavelet neural network in time series prediction and system modeling based on multiresolution learning. IEEE Conf Syst Man Cybern 1: 425–430

    Google Scholar 

  6. Chen Y, Yang B, Dong J (2006) Time-series prediction using a local linear wavelet neural network. J Neurocomput 449–465

  7. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE conference on neural networks, pp 1942–1948

  8. Mosavi MR (2006) A practical approach for accurate positioning with L1 GPS receivers using neural networks. J Intell Fuzzy Syst 17(2): 159–171

    Google Scholar 

  9. Mosavi MR (2007) GPS receivers timing data processing using neural networks: optimal estimation and errors modeling. J Neural Syst 17((5): 383–393

    Article  Google Scholar 

  10. Widrow B, Lehr MA (1990) 30 Years of adaptive neural networks: perceptron, madaline, and backpropagation. Proc IEEE 78(9): 1415–1442

    Article  Google Scholar 

  11. Cai X, Zhang N, Venayagamoorthy GK, Wunsch II DC (2007) Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm. J Neurocomput 2342–2353

  12. Li SJ, Zhang XJ, Qian F (2005) Soft sensing modeling via artificial neural network based on PSO-Alopex. In: IEEE conference on machine learning and cybernetics, pp 4210–4215

  13. Refan MH, Mohammadi K, Mosavi MR (2003) Improvement on a low cost positioning sensor accuracy. IEEE Conf Sens (1): 9–14

  14. Øvstedal O (2002) Absolute positioning with single-frequency GPS receivers. J GPS Solut 5(4): 33–44

    Article  Google Scholar 

  15. Sang J, Kubik K, Zhang L (1997) Prediction of DGPS Corrections with Neural Networks.IEEE Conference on Knowledge-based Intelligent Electronic Systems:355–361

  16. Indriyatmoko A, Kang T, Lee YJ, Jee GI, Cho YB, Kim J (2008) Artificial Neural Networks for Predicting DGPS Carrier Phase and Pseudo-range Correction. Journal of GPS Solution 12(4): 237–247

    Article  Google Scholar 

Download references

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  1. Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran

    M. R. Mosavi

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  1. M. R. Mosavi
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Correspondence to M. R. Mosavi.

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Mosavi, M.R. Wavelet Neural Network for Corrections Prediction in Single-Frequency GPS Users. Neural Process Lett 33, 137–150 (2011). https://doi.org/10.1007/s11063-011-9169-x

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