Recognition of marine objects in thermal imaging surveillance and sighting systems of aircraft

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Abstract

The problem of object recognition based on information from measurements of their geometric features in images of optoelectronic systems is considered. The probabilistic characteristics of the measurements are known. A recognition algorithm has been developed using the theory of systems with a random discontinuous structure [1] and the method of two-stage parametric approximation of distributions [2]. The approximation of the predicted probability density by the beta distribution law is applied. The results of the calculation of the algorithm based on the information of the sequence of images obtained in full-scale registrations are presented.

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About the authors

V. A. Bukhalev

Moscow Scientific Research Television Institute

Email: abu-sergey@yandex.ru
Russian Federation, Golyanovskaya Str., 7a, build. 1, Moscow, 105094

I. F. Khismatov

Moscow Scientific Research Television Institute

Author for correspondence.
Email: abu-sergey@yandex.ru
Russian Federation, Golyanovskaya Str., 7a, build. 1, Moscow, 105094

A. A. Skrynnikov

The State Scientific Research Institute of Aviation Systems; Moscow Aviation Institute (National Research University)

Email: abu-sergey@yandex.ru
Russian Federation, Viktorenko Str., 7, Moscow, 125167; Volokolamskoe shosse, 4, Moscow, 125993

V. A. Boldinov

The State Scientific Research Institute of Aviation Systems; Moscow Aviation Institute (National Research University)

Email: abu-sergey@yandex.ru
Russian Federation, Viktorenko Str., 7, Moscow, 125167; Volokolamskoe shosse, 4, Moscow, 125993

K. Y. Rozhkov

Moscow Scientific Research Television Institute

Email: abu-sergey@yandex.ru
Russian Federation, Golyanovskaya Str., 7a, build. 1, Moscow, 105094

References

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  2. Бухалëв В.А., Скрынников А.А., Болдинов В.А. Двухмоментная параметрическая аппроксимация распределений. М.: Физматлит, 2023.
  3. Бельский А.Б., Чобан В.М. // Труды МАИ. 2013. № 66. С. 23.
  4. Левшин Е.А., Хисматов И.Ф. Моделирование и оценка авиационных оптико-электронных систем самонаведения. Воронеж: Научная книга, 2022.
  5. Бухалëв В.А., Скрынников А.А., Болдинов В.А. Алгоритмическая помехозащита беспилотных летательных аппаратов. М.: Физматлит, 2018.
  6. Казаков И.Е., Доступов Б.Г. Статистическая динамика нелинейных автоматических систем. М.: Физматгиз, 1962.
  7. Пугачëв В.С. Теория случайных функций и ее применение к задачам автоматического управления. М.: Физматгиз, 1960.
  8. Ярлыков М.С., Миронов М.А. Марковская теория оценивания случайных процессов. М.: Радио и связь, 1993.
  9. Корн Р., Корн Т. Справочник по математике для научных работников и инженеров. М.: Наука, 1984.

Supplementary files

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2. Fig. 1. Normal distribution for μ = 0.8, β = 0.1 (curve 1); μ = 0.6, β = 0.05 (curve 2); μ = 0.4, β = 0.05 (curve 3).

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3. Fig. 2. Beta distribution for α = 1, β = 3 (curve 1); α = 7, β = 1 (curve 2); α = 0.5, β = 0.5 (curve 3).

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4. Fig. 3. Frame of the ship image.

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5. Fig. 4. Results of modeling the identifier using formula (10) and recognition using the rule of formula (21).

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6. Fig. 5. Results of modeling the first distinguishing feature of the first type of ship, errors of its measurements and filtering.

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7. Fig. 6. Results of modeling the second distinguishing feature of the first type of ship, errors in its measurements and filtering.

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