Abstract
The ionosphere plays an important role in radio applications, e.g., satellite, cellular phone, global positioning system (GPS), etc. An ionogram is one of the information sources of the ionosphere. Unfortunately, ionograms are generally corrupted by noise and artifacts. These imperfections cause difficulties in the efforts to create automatic systems. In this paper, we propose a size-contrast filtering-based ionogram enhancement. The size-contrast filter can suppress the objects that are too small or too large. The good enhancement performance is achieved by applying the proposed algorithm to the ionograms collected by the ionosonde at Chiang Mai University, Thailand. We also propose a critical frequency estimation algorithm for the ordinary-mode and extraordinary-mode wave components. The proposed estimation algorithm is based on the region of interest selection, and weak segment elimination and trace determination. We achieve very good estimation performance by evaluating our proposed algorithms on ionograms from 2 sets of 12 consecutive sweeps from the nighttime and daytime.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arikan, F., Arikan, O., Salous, S.: A New Algorithm for High-Quality Ionogram Generation and Analysis. Radio Science 37(1), 4–11 (2002)
Arikan, F., Salous, S., Arikan, O.: A New Algorithm for High Quality Ionograms. Electronics Letters 36(11), 985–987 (2000)
Redding, N.J.: Image Understanding of Oblique Ionograms: The Autoscaling Problem. In: Proc. 1996 Australian New Zealand Conf. on Intelligent Information Systems (1996)
Barlett, A., Gallagher, M., Darnell, M.: Extraction, Analysis and Interpretation of Digital Ionogram. In: Proc. 6th IEEE Intl. Conf. on HF Radio Systems and Techniques, pp. 278–282 (1994)
Kettler, D.I., Redding, N.J.: A Trimming Algorithm to Clean Thinned Feature for Feature Extraction in Image Understanding. In: Proc. 1996 Australian New Zealand Conf. on Intelligent Information Systems, pp. 304–307 (1996)
Zain, A.F.M., Abdullah, M.: Application of Artificial Intelligence to Ionospheric Radio Propagation. Proc. IEEE TENCON 1, 82–87 (2000)
Chan, A., Cannon, P.S.: Degradation in Neural Network Prediction Models of f0F2 with Time. Proc. IEE Intl. Conf. on Antennas and Propagation 2, 787–791 (2001)
Cander, L.R., Bamford, R., Hickford, J.G.: Nowcasting and Forecasting the foF2, MUF(3000)F2 and TEC Based on Empirical Models and Real-Time Data. In: Proc. Intl. Conf. on Antennas and Propagation. vol. 1, pp. 139–142 (2003)
Roostaei, S., Sadat, S., Ghobadi, C., Nourinia, J.: Modeling and Numerical Analysis of the Earth-Ionosphere Waveguide using Finite Difference Method. In: Proc. Intl. Symp. on Antennas and Propagation Society. vol. 2B, pp. 158–161 (2005)
Chapin, E., Chan, S.F., Chapman, B.D., Chen, C.W., Martin, J.M., Michel, T.R., Muellerschoen, R.J., Pi, X., Rosen, P.A.: Impact of the Ionosphere on an L-band Space Based Radar. In: Proc. IEEE Conf. on Radar, pp. 51–58 (2006)
Theera-Umpon, N., Gader, P.D., Ho, D.K.: Region-Based Time-Domain Processing of Hand-Held GPR. In: UXO Countermine Forum (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Theera-Umpon, N. (2007). Ionospheric F-Layer Critical Frequency Estimation from Digital Ionogram Analysis. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74484-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-74484-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74482-5
Online ISBN: 978-3-540-74484-9
eBook Packages: Computer ScienceComputer Science (R0)