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• ESTIMATION OF THE SPATIAL POSITION OF AN ON-BOARD CAMERA BY COMPARING AERIAL IMAGES AND SATELLITE IMAGE DATA

  А.Y. Budko , Т.А. Gaida , Z.А. Ponimash

  2026-02-27

  Abstract ▼

  The article describes a method for estimating the spatial position of an onboard camera of an aircraft. This method involves comparing aerial photographs and georeferenced remote sensing (RSS) data by using a neural network detector to detect stable spatiotemporal reference points in both datasets. This method then solves the well-known Perspective-n-Point (PnP) problem for estimating rotation and translation matrices that minimize the reprojection error based on the correspondences between 3D world points and 2D points of their projections onto the onboard camera matrix. This approach can be used to solve the pressing problem of aircraft localization in the absence of global navigation satellite system signals. Road intersections are selected as stable spatiotemporal reference points that are clearly visible in RSS data and aerial photographs. Other local semantic image patterns characteristic of a particular area may serve as an alternative. Since direct comparison of remote sensing and airborne images is difficult due to significant differences in shooting conditions, the use of robust landmark detectors based on artificial neural network (ANN) algorithms is proposed. To train the robust detector, a mixed dataset was created using satellite and airborne imagery. The mixed dataset was labeled using a 3D Gaussian function normalized to unity with a apex at the intersection center, the graph of which is projected onto a 2D mask of the training set. The parameters of the Gaussian function are calculated based on the radius of the circle enclosing the intersection. Using a normalized 3D Gaussian function with a apex at the geometric center of the intersection projection allows the network to predict the probability of each image pixel belonging to the intersection, with a maximum at the intersection center, which increases positioning accuracy due to more precise georeferencing of the landmark point in the global 3D dataset. A U-Net-type artificial neural network was trained as an intersection detector. A differentiable analog of the Dice metric was used as a training quality metric. AdamW, coupled with a CosineAnnealingLR cosine learning rate planner, was used as an optimizer. The final section of the paper presents the results of comparing satellite data and airborne imagery using the proposed method.

• METHOD AND ALGORITHM FOR EXTRACTING FEATURES FROM DIGITAL SIGNALS BASED ON NEURAL NETWORKS TRANSFORMER

  Z.А. Ponimash, М.V. Potanin

  52-64

  2025-01-13

  Abstract ▼

  Recently, neural network models have become one of the most promising directions in the field of automatic
  feature extraction from digital signals. Traditional approaches, such as statistical, time-domain,
  frequency-domain, and time-frequency analysis, require significant expert knowledge and often prove insufficiently
  effective when dealing with non-stationary and complex signals, such as biomedical signals (ECG,
  EEG, EMG) or industrial signals (e.g., currentgrams). These methods have several limitations when it comes
  to analyzing multichannel data with varying frequency structures or when signal labeling is too laborintensive
  or expensive. Modern neural network architectures, such as transformers, have demonstrated high
  efficiency in automatic feature extraction from complex data. Transformers have outperformed traditional
  convolutional and recurrent neural networks in many key metrics, particularly in tasks involving time series
  forecasting, multimodal data classification, and feature extraction from sequences. Their ability to model
  complex temporal dependencies and nonlinear relationships in data makes them ideal for tasks such as noise
  filtering and multimodal signal processing. This paper proposes a method for feature extraction from digital
  signals based on a modified transformer architecture that incorporates a nonlinear layer after the selfinspection
  module. This approach improved the ability of the model to detect complex and nonlinear dependencies
  in the data, which is particularly important when dealing with biomedical and signals obtained from
  industrial systems. A description of the architecture and the experiments performed are presented, demonstrating
  the high performance of the model in solving signal classification, prediction and filtering problems.
  It is expected that the model can be applied to a wide range of applications including disease and fault
  diagnosis, signal parameter prediction and system modelling.

• METHOD AND ALGORITHM FOR SIGNAL SIMULATION IN LOCATION AND WIRELESS COMMUNICATIONS SYSTEMS WITH MOVING GEOMETRY

  А.А. Maryev, Z.А. Ponimash

  2024-01-05

  Abstract ▼

  The work is devoted to the issues signals simulation in systems with moving objects. The relevance
  of the problem is determined by the growing interest in application of ultra-wideband signals,
  progress in the field of creation of hypersonic aircraft and low-orbit spacecraft, as well as
  the widespread use of radar and sonar systems with long accumulation of signals.
  The geometry of the problem of location for a rather general case (bistatic location with moving
  transmitter, reflector and receiver) is considered, as well as the method and algorithm for solving
  the problem of modeling echo signal for the simplified case of homogeneous and isotropic medium.
  The necessity of numerical methods usage for realization of the proposed simulation method,
  suggestions on the choice of numerical methods are given: Runge-Kutta method is suggested for
  solving differential equations, for solution of algebraic equations Newton's method is suggested.
  Recommendations are given on the choice of parameters of each of the numerical methods. The
  applicability of the proposed method and algorithm to the problem of wireless communication with
  mobile objects is shown. Several important special cases for each of the problems are considered
  with the indication of areas of radio engineering and hydroacoustics, in which each of the special
  cases are relevant. It is shown that in a number of simple special cases the proposed method leads
  to solutions already obtained by other authors and published in open sources. Recommendations
  are given on generalization of the algorithm to more complex variants of the problem formulation