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Researcher profile

Oleg I. Berngardt

Oleg I. Berngardt contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
physics.ao-phphysics.geo-phphysics.space-phphysics.data-anComputer VisionMachine Learning

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Published work

4 published item(s)

preprint2026arXiv

Physically-Informed Fuzzy Clustering of Vertical Sounding Ionograms

This paper presents a physically-informed fuzzy clustering of vertical sounding ionograms for automatically separating the ionogram into tracks suitable for further interpretation and determining their optimal number. The model is designed for use not only in conditions where the number of tracks is known, but also in disturbed ionospheric conditions where the number of tracks is preliminary unknown. The method is based on an expectation-maximization algorithm, used for clustering, and on parametrically specified distributions of distances from points to parametrically specified curves. The curves used as track models are close to model tracks in the parabolic ionospheric layer model. The resulting model of each track has six parameters: three standard ones (the critical frequency, the lower boundary of the layer, and its half-width), and three additional ones to take into account possible underlying layer effects. By sequentially increasing the number of tracks and optimizing their parameters, the model finds the optimal number of tracks on the ionogram by minimizing the modified Bayesian information criterion. The Sequential Least Squares Quadratic Programming algorithm is used to find the parameters of a single track. The width of each single track is assumed to be unknown constant found during fitting process. To improve the quality of ionogram clustering, automatic adaptive noise filtering is performed before clustering. This filtering is based on a combination of the DBSCAN and Gaussian Mixture algorithms. Also, to improve clustering quality on an ionosonde without hardware separation of the ordinary and extraordinary components, a preliminary approximate removal of points belonging to the extraordinary mode is performed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Wrapped Classifier with Dummy Teacher for training physics-based classifier at unlabeled radar data

In the paper a method for automatic classification of signals received by EKB and MAGW ISTP SB RAS coherent scatter radars (8-20MHz operating frequency) during 2021 is described. The method is suitable for automatic physical interpretation of the resulting classification of the experimental data in realtime. We called this algorithm Wrapped Classifier with Dummy Teacher. The method is trained on unlabeled dataset and is based on training optimal physics-based classification using clusterization results. The approach is close to optimal embedding search, where the embedding is interpreted as a vector of probabilities for soft classification. The approach allows to find optimal classification algorithm, based on physically interpretable parameters of the received data, both obtained during physics-based numerical simulation and measured experimentally. Dummy Teacher clusterer used for labeling unlabeled dataset is gaussian mixture clustering algorithm. For algorithm functioning we extended the parameters obtained by the radar with additional parameters, calculated during simulation of radiowave propagation using ray-tracing and IRI-2012 and IGRF models for ionosphere and Earth's magnetic field correspondingly. For clustering by Dummy Teacher we use the whole dataset of available parameters (measured and simulated ones). For classification by Wrapped Classifier we use only well physically interpreted parameters. As a result we trained the classification network and found 11 well-interpretable classes from physical point of view in the available data. Five other found classes are not interpretable from physical point of view, demonstrating the importance of taking into account radiowave propagation for correct classification.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Comparison of AATR and WTEC indices in the studies of the level of ionospheric disturbance

A comparative statistical analysis of AATR and WTEC indices was conducted based on data from the ISTP SB RAS GNSS receivers network. It is shown that at high levels of ionospheric disturbance (for WTEC > 0.1 TECU), the AATR index is proportional to the WTEC index with a factor of $1.5min^{-1}$. At small levels of ionospheric disturbance (for WTEC < 0.1 TECU), this proportionality is violated. It is shown that the contribution of daily dynamics of the background ionosphere to the AATR index is higher than to the WTEC index. This leads to a higher sensitivity of the WTEC index to disturbances. This also leads to violating the proportionality between WTEC and AATR indices at low levels of ionospheric disturbance. It is shown that at high latitudes the dynamics of the WTEC and AATR indices correlate significantly with the level of geomagnetic disturbance Kp. At mid-latitudes, the contribution of solar radiation variations (F10.7 index) and vertical seismic variations exceeds the influence of Kp variations. The program for calculating WTEC indices, used in the paper is put into open access.

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preprint2019arXiv

Noise level forecasts at 8-20MHz and their use for morphological studies of ionospheric absorption variations at EKB ISTP SB RAS radar

In this paper, a method is described for using 8-20MHz noise absorption effect for realtime detecting radiowave absorption periods. The method is based on two empirical autoregression models of noise dynamics. The first (rough) prediction model is based on radar measurements of daily minimal noise dynamics averaged over 28-days with specially calculated weight coefficients. The second (fine) prediction model uses real-time scaling of rough model. The scaling is based on the comparison of this model with the experimental noise observations during previous 5 days. The models are based on the whole EKB ISTP SB RAS radar dataset (2013-2018). The rough model allows one to estimate the boundary beyond which the noise variations can be associated with absorption periods with a high degree of certainty. A joint analysis of simultaneous data on neighboring radar beams and at several frequencies reduces the detection errors, and allows to identify absorption events with a higher degree of confidence. Use of fine model allows to estimate absorption. The technique is validated by frequency dependence of absorption during two-frequency measurements. The found frequency dependence has an average exponent of the order of -1.5, which is in good agreement with the literature data and the data obtained earlier in analysis of solar X-ray flares. The use of the detection technique at EKB radar shows that most probable absorption over absorption events is about -0.65dB. Analysis of absorption of different amplitudes shows that low-intensity absorption events (0..-1.3dB) have slight local time dependence and mostly observed at north directions. For the storng absorption events (stronger than -1.3dB) the local time dynamics correlates well with noise level dynamics, and usually fills the whole radar field-of-view.

0 citations0 reviewsNo code linkedOpen access