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Optimized design and analysis of offshore Beidou maritime foundation reinforcement system pseudolite ranging-codes

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Under the bad sea conditions and weak Beidou navigation signal environment, it is difficult to locate the ship on the sea, and the Beidou navigation system can’t work well. Beidou pseudolite system can improve the performance of Beidou navigation system as a navigation signal transmitter fixed on the ground, the signal of which can improve satellite positioning constellation structure, and improve the system availability reliability and precision. In order to ensure the interoperability and non-correlation of the Beidou pseudolite and the Beidou navigation system, the pseudolite ranging-codes should be selected in the same code space of the satellite ranging-codes and the residual pseudo random code generated by the 2 taps design scheme does not satisfy the performance requirements of the ranging-codes. To solve this issue, a combined design scheme with 3 taps is proposed, and 12 kinds of pseudolite ranging-codes are optimally selected according to the optimum design parameters of ranging-codes. The waveform and correlation of the selected pseudolite ranging-codes are analyzed by MATLAB, the acquisition simulation is carried out by using the new pseudolite ranging-codes. The experimental results show that the new 3-taps based ranging codes design scheme behaves a good balance, correlation and spectral characteristics.
Słowa kluczowe
Rocznik
Tom
S 2
Strony
45--52
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
autor
  • School of Information Engineering Wuhan University of Technology, Wuhan Hubei 430074 China, tel.: 13093212998
autor
  • School of Information Engineering, Wuhan University of Technology, Wuhan, 430074, China
autor
  • China Transport Telecommunications and Information Center, Beijing, 100011, China
autor
  • School of Automation, Wuhan University of Technology, Wuhan, 430074, China
Bibliografia
  • 1. Ji L, Shan Q: The Development Outline and Latest Evolution of Global Navigation Satellite System. GNSS World of China, Vol. 37, no. 5, pp. 56-61, 2012.
  • 2. Dong S: The Compass Time System and Its Interoperation. APPLIED MECHANICS AND MATERIALS, Vol. 239, pp. 548-551, 2012.
  • 3. Sun F, Liu S, Zhu X: Research and Progress of Beidou Satellite Navigation System. Sciece China: Information Sciences, Vol. 55, no. 12, pp. 2899-2907, 2012.
  • 4. Yang C, Lu X, Wang X: Performance Analysis for Ranging Codes of Satellite Navigation System. Journal of Time and Frequency, Vol. 36, no. 3, pp. 173-180, 2013.
  • 5. TANG J: Developing and Applying Analysis of BeiDou Navigation Satellites Regional System. Global Positioning System, Vol. 38, no. 5, pp. 47-52, 2013.
  • 6. Montenbruck O, Hauschild A, Steigenberger P, Hugentobler U, Teunissen Pand Nakamura S: Initial assessment of the COMPASS/ BeiDou-2 regional navigation satellite system. GPS Solutions, Vol. 17, no. 2, pp. 211-222, 2012.
  • 7. Wu T, Zhang Y, Liu Y: BD/GPS integrated positioning method. Remote Sensing, Vol. 5, pp. 1087-1097, 2014.
  • 8. WANG X, FAN A, FU Z: The Design of BeiDou Pseudo-satellite Compatible Receiver. Wuhan: China Satellite Navigation Conference, 2013.
  • 9. Liu D, BO Y, WU P: The Research on DOP of Beidou Positioning System Using Pseudolite. Fire Control & Command Control. Vol. 34, no. 7, pp. 137-139, 2009.
  • 10. Hikawa H: Frequency-based multilayer neural network with on-chip learning and enhanced neuron characteristics. IEEE Transactions on Neural Networks, Vol. 10, no. 3, pp. 545-53, 1995.
  • 11. Serpen G, Gao Z: Complexity Analysis of Multilayer Perceptron Neural Network Embedded into a Wireless Sensor Network. Procedia Computer Science, Vol. 36, pp.192-197, 2014.
  • 12. Kumar A, Joshi H, P. S: Neural Network Approach for Automatic Landuse Classification of Satellite Images: One-Against-Rest and Multi-Class Classifiers. International Journal of Computer Applications, pp.134, 2016.
  • 13. Raza M Q, Khosravi A: A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable & Sustainable Energy Reviews, Vol. 50, pp.1352-1372, 2015.
  • 14. Hosseini S M S, Maleki A, Gholamian M R: Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, Vol. 37, no. 7, pp.5259-5264, 2010.
  • 15. Liu Y F, Li M Y: Application of Data Mining Technology in CRM. Applied Mechanics & Materials, Vol. 513, pp. 2103-2106, 2014.
  • 16. Maheswari R U, Mahesan S S: Role of Data Mining in CRM. International Journal of Engineering Research, Vol. 3, no. 2, pp. 75-78, 2014.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-86c3e44f-da31-48e9-9307-a3002f7c47ca
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