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Mathematical Model Development for Navigation with Indian Constellation (NavIC) L-Band Geo Synchronous Satellite's Direct Signal and Multipath Signals

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Abstract

NavIC L-band satellite signal that travels from the satellite to the receiver undergoes mainly shadowing and multipath effects. In this research paper, NavIC satellites were observing and analyzing signal efficiency for both the direct signal and multipath signal over the Dehradun area. Two open space data series (direct signal and multipath signal) for Dehradun were obtained in this analysis by the satellite receiver. The methodology includes evaluation, testing, and analysis of data in both cases. Based on collected data, a general mathematical model has been developed, describing the signal intensity in the open space context. The both NavIC Mathematical model was validated with the experimental data. The average R2 and RMSE values developed for the direct signal mathematical model were 0.89 and 1.08% respectively which show a good prediction of NavIC C/N0 value with the developed model. For the multipath mathematical model, four cases have been evaluated. Comparing the graphical view of all four cases for C/N0 multipath signal concerning raw NavIC C/N0 multipath signal has been done to select the best mathematical model.

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References

  1. Deep, S., Raghavendra, S., & Bharath, B. D. (2018). GPS SNR prediction in urban environment. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 83–85.

    Article  Google Scholar 

  2. Pai, B. V., Abidin, W. A. W., Othman, A. K., Zen, H., & Masri, T. (2011). Characteristics of mobile satellite L-band signal in mid-latitude region: GPS approach. 84.40. Ua.

  3. Han, M., Zhu, Y., Yang, D., Hong, X., & Song, S. (2018). A semi-empirical SNR model for soil moisture retrieval using GNSS SNR data. Remote Sensing, 10(2), 280. https://doi.org/10.3390/rs10020280

    Article  Google Scholar 

  4. Larson, K. M., Small, E. E., Gutmann, E., Bilich, A., Axelrad, P., & Braun, J. (2008). Using GPS multipath to measure soil moisture fluctuations: Initial results. GPS Solutions, 12(3), 173–177.

    Article  Google Scholar 

  5. Zavorotny, V. U., Larson, K. M., Braun, J. J., Small, E. E., Gutmann, E. D., & Bilich, A. L. (2009). A physical model for GPS multipath caused by land reflections: Toward bare soil moisture retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(1), 100–110. https://doi.org/10.1109/JSTARS.2009.2033608

    Article  Google Scholar 

  6. Richter, B., & Euler, H. J. (2001). Study of improved observation modeling for surveying type applications in multipath environment. In Proceedings of the 14th international technical meeting of the satellite division of the institute of navigation (ION GPS 2001) (pp. 1048–1055).

  7. Byun, S. H., Hajj, G. A., & Young, L. E. (2002). Development and application of GPS signal multipath simulator. Radio Science, 37(6), 1–23.

    Article  Google Scholar 

  8. Bhardwaj, S. C., Vidyarthi, A., Jassal, B. S., & Shukla, A. K. (2017). Study of temporal variation of vertical TEC using NavIC data. In 2017 International conference on emerging trends in computing and communication technologies (ICETCCT) (pp. 1–5). IEEE.

  9. Chamoli, V., Prakash, R., Vidyarthi, A., & Ray, A. (2017). Sensitivity of NavIC signal for soil moisture variation. In 2017 International conference on emerging trends in computing and communication technologies (ICETCCT) (pp. 1–4). IEEE.

  10. Pandey, J., Prakash, R., Ray, A., Chamoli, V., & Vidyarthi, A. (2019). Study of GPS C/No ratio for retrieval of surface soil moisture. In 2019 International conference on signal processing and communication (ICSC) (pp. 213–216). IEEE.

  11. Pandey, J., Chamoli, V., & Prakash, R. (2020). A review: Soil moisture estimation using different techniques. In S. Choudhury, R. Mishra, A. Kumar (Eds.), Intelligent communication, control and devices (pp. 105–111). Singapore: Springer.

  12. Chamoli, V., Prakash, R., Vidyarthi, A., & Barthwal, S. (2021). Ground truth soil moisture estimation along with minimal drizzling time. International Journal of Modern Agriculture, 10(2), 2692–2698.

    Google Scholar 

  13. Chamoli, V., Prakash, R., Vidyarthi, A., & Ray, A. (2021). Analysis of NavIC multipath signal sensitivity for soil moisture in presence of vegetation. In International conference on innovative computing and communications (pp. 353–364). Singapore: Springer.

  14. Chamoli, V., Prakash, R., Vidyarthi, A., & Ray, A. (2020). Capability of NavIC, an Indian GNSS constellation, for retrieval of surface soil moisture. Progress in Electromagnetics Research, 106, 255–270.

    Article  Google Scholar 

  15. Shekhar, S., Prakash, R., Vidyarthi, A., & Pandey, D. K. (2020). Sensitivity analysis of navigation with Indian constellation (NavIC) derived multipath phase towards surface soil moisture over agricultural land. In 2020 6th International conference on signal processing and communication (ICSC) (pp. 138–142). IEEE.

  16. Johri, A., Prakash, R., Vidyarthi, A., Chamoli, V., & Bhardwaj, S. (2021). IoT-based system to measure soil moisture using soil moisture sensor, GPS data logging and cloud storage. In International conference on innovative computing and communications (pp. 679–688). Singapore: Springer.

  17. Prakash, R., Singh, D., & Pathak, N. P. (2009). Microwave specular scattering response of soil texture at X-band. Advances in Space Research, 44(7), 801–814.

    Article  Google Scholar 

  18. Wan, W., Li, H., Chen, X., Luo, P., & Wan, J. (2013). Preliminary calibration of GPS signals and its effects on soil moisture estimation. Acta Meteorologica Sinica, 27(2), 221–232.

    Article  Google Scholar 

  19. Phillips, A. J., Newlands, N. K., Liang, S. H., & Ellert, B. H. (2014). Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support. Computers and Electronics in Agriculture, 107, 73–88.

    Article  Google Scholar 

  20. Liang, W. L., Hung, F. X., Chan, M. C., & Lu, T. H. (2014). Spatial structure of surface soil water content in a natural forested headwater catchment with a subtropical monsoon climate. Journal of Hydrology, 516, 210–221.

    Article  Google Scholar 

  21. Tabibi, S., Nievinski, F. G., van Dam, T., & Monico, J. F. (2015). Assessment of modernized GPS L5 SNR for ground-based multipath reflectometry applications. Advances in Space Research, 55(4), 1104–1116.

    Article  Google Scholar 

  22. Zhang, D., Li, Z. L., Tang, R., Tang, B. H., Wu, H., Lu, J., & Shao, K. (2015). Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions. International Journal of Remote Sensing, 36(19–20), 5015–5030.

    Article  Google Scholar 

  23. El Hajj, M., Baghdadi, N., Zribi, M., Belaud, G., Cheviron, B., Courault, D., & Charron, F. (2016). Soil moisture retrieval over irrigated grassland using X-band SAR data. Remote Sensing of Environment, 176, 202–218.

    Article  Google Scholar 

  24. Liao, W., Wang, D., Wang, G., Xia, Y., & Liu, X. (2019). Quality control and evaluation of the observed daily data in the North American soil moisture database. Journal of Meteorological Research, 33(3), 501–518.

    Article  Google Scholar 

  25. Tabibi, S., Geremia-Nievinski, F., & van Dam, T. (2017). Statistical comparison and combination of GPS, GLONASS, and multi-GNSS multipath reflectometry applied to snow depth retrieval. IEEE Transactions on Geoscience and Remote Sensing, 55(7), 3773–3785.

    Article  Google Scholar 

  26. Li, Z., Chen, P., Zheng, N., & Liu, H. (2021). Accuracy analysis of GNSS-IR snow depth inversion algorithms. Advances in Space Research, 67(4), 1317–1332.

    Article  Google Scholar 

  27. Li, Y., Chang, X., Yu, K., Wang, S., & Li, J. (2019). Estimation of snow depth using pseudorange and carrier phase observations of GNSS single-frequency signal. GPS Solutions, 23(4), 1–13.

    Article  Google Scholar 

  28. Pitman, A. J. (2003). The evolution of, and revolution in, land surface schemes designed for climate models. International Journal of Climatology: A Journal of the Royal Meteorological Society, 23(5), 479–510.

    Article  Google Scholar 

  29. Istanbulluoglu, E., & Bras, R. L. (2006). On the dynamics of soil moisture, vegetation, and erosion: Implications of climate variability and change. Water Resources Research, 42(6).

  30. Sinha, S., Mathur, R., Bharadwaj, S. C., Vidyarthi, A., Jassal, B. S., & Shukla, A. K. (2018). Estimation and smoothing of TEC from NavIC dual frequency data. In 2018 4th International conference on computing communication and automation (ICCCA) (pp. 1–5). IEEE

  31. Chamoli, V., Prakash, R., & Vidyarthi, A. (2020). Mathematical regression model to predict navigation with Indian constellation (NavIC) Geo synchronous satellite system. In 2020 Global conference on wireless and optical technologies (GCWOT) (pp. 1–5). IEEE.

  32. Motte, E., Egido, A., Roussel, N., Boniface, K., & Frappart, F. (2016). Applications of GNSS-R in continental hydrology. In N. Baghdadi, M. Zribi (Eds.), Land surface remote sensing in continental hydrology (pp. 281–322). Elsevier.

  33. Stroosnijder, L., Lascano, R. J., Van Bavel, C. H. M., & Newton, R. W. (1986). Relation between L-band soil emittance and soil water content. Remote Sensing of Environment, 19(2), 117–125.

    Article  Google Scholar 

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Funding

This work is supported by the Space Applications Center (SAC), Indian Space Research Organization (ISRO), Ahmedabad India under NavIC—GAGAN Utilization Program.

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Correspondence to Vivek Chamoli.

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Chamoli, V., Prakash, R. & Vidyarthi, A. Mathematical Model Development for Navigation with Indian Constellation (NavIC) L-Band Geo Synchronous Satellite's Direct Signal and Multipath Signals. Wireless Pers Commun 126, 3367–3388 (2022). https://doi.org/10.1007/s11277-022-09869-7

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Keywords

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