Browsing by Subject "Ionosonde"

Now showing 1 - 3 of 3

Open Access
Ionogram scaling using Hidden Markov Models
(Institute of Electrical and Electronics Engineers, 2018) Gök, Gökhan; Alp, Y. K.; Arıkan, Orhan; Arikan, F.
In this paper, a novel method for electron density reconstruction using ionosonde data is proposed. Proposed technique uses Hidden Markov Models for extracting echoes that provides valuable information about electron density distribution in order to provide input to a model based optimization technique that reconstructs the electron density distribution by solving model parameters. Analysis on real ionosonde data shows that proposed technique outperforms standard techniques in the literature.
Open Access
A method for automatic scaling of ionograms and electron density reconstruction
(IEEE, 2021-10-19) Gök, Gökhan; Alp, Y. K.; Arıkan, Orhan; Arıkan, F.
Ionogram scaling is the process of reconstructing electron density with respect to height by using the measurements of a remote sensing instrument known as ionosonde. In this study, a novel two stage ionogram scaling technique, ISED, is proposed. In the first stage, Hidden Markov Models (HMMs) are used to identify the actual ionospheric reflections in the ionosonde measurements. In the second stage, an IRI-Plas model based optimization problem is solved to obtain the vertical profile that generates the best least squares fit to the reflections identified in the first stage. To show the performance of ISED in global scale, experiments are conducted on 14,812 ionograms recorded at the three different stations which are Pruhonice in Czech Republic, Eielson in USA and Sao Luis in Brazil. Application of ISED to raw ionograms indicate 97.6% of the cases, ISED provides accurate electron density reconstructions, which is an improvement about 8.7% over ARTIST, most commonly used ionogram scaling technique.
Open Access
Novel signal processing techniques for remote sensing applications
(2021-06) Gök, Gökhan
Remote sensing is the acquisition of information about an object or phenomenon without a direct contact. In this thesis, novel signal processing techniques are pro-posed for two diﬀerent remote sensing applications. First, for the reconstruction of electron density proﬁle in the ionosphere using the ionosonde measurements, a new technique, ISED, is proposed. By using a hidden Markov model, ISED technique ﬁrst identiﬁes actual ionosonde echoes reﬂected from the ionosphere. Then, vertical electron density proﬁle is estimated as the solution to a model based convex optimization problem. Conducted experiments on real ionosonde data show that ISED overperforms the state of the art ionogram inversion tools. Second part, for Speciﬁc Emitter Identiﬁcation (SEI) of radar transmitters, a new SEI technique is proposed. SEI is an important Electronic Warfare (EW) activ-ity that aims to identify unique transmitters, even the emitters of same kind, by using the subtle diﬀerences on the transmitted signals. Proposed SEI consist of two main stages. In the ﬁrst stage, received radar pulses are time aligned and co-herently integrated to reveal subtle diﬀerences which could be used to distinguish diﬀerent radar transmitters. In the second step, Variational Mode Decomposition (VMD) is used to decompose both the envelope and the instantaneous frequency of the received radar signal into a set of modes. Then, these mod signals are characterized by using a group of features for identiﬁcation. Highly successful identiﬁcation performance with the proposed method on real radar datasets is demonstrated.