Model for estimating statistical characteristics of the pre-stroke warehouse process based on average monthly temperatures analysis

Authors

  • V.A. Tikhonov Харківський національний університет радіоелектроніки, Ukraine https://orcid.org/0000-0002-4618-4787
  • V.M. Kartashov Харківський національний університет радіоелектроніки, Ukraine https://orcid.org/0000-0001-8335-5373
  • O.V. Kartashov Харківський національний університет радіоелектроніки, Ukraine

DOI:

https://doi.org/10.30837/rt.2022.2.209.24

Keywords:

non-stationary processes, model, autoregression, moving average, long-term trends, temperature, trajectory, seasonal component

Abstract

The possibilities of an improved autoregression model and an integrated moving average (ARMAS) for the analysis of non-stationary data and the identification of long-term trends in the processes under study are considered. The proposed model can be used to study the observed processes in various areas of human activity: the analysis of the observed trajectories of the movement of aircraft, in particular unmanned aerial vehicles, meteorological processes that reflect the state of the atmosphere. The mathematical apparatus developed in the article was used to analyze changes in the atmospheric temperature time series observed for a long time, the average annual temperatures were estimated, followed by sliding smoothing with a low-frequency filter.

It is shown that the removal of the seasonal component in the ARPSS model eliminates or distorts significantly the trend and has little effect on the stationary component of the ARPSS process. The operation of de-trending has little effect on the properties of the seasonal component and the stationary component of the process. To assess the trend, the mean annual temperatures were preliminarily obtained. The use of moving averaging, which removes the seasonal component from the average monthly temperatures, makes it possible to find a weak long-term trend. The results obtained in the work can be used to analyze medium-term and long-term changes in atmospheric phenomena, to refine the results obtained by traditional methods of processing results and methods of mathematical statistics, as well as in other areas of human activity.

References

Кошкин Р.П. Беспилотные авиационные системы. Москва : Стратегические приоритеты, 2016. 676 с.

Макаренко С. И., Тимошенко А. В., Васильченко А. С. Анализ средств и способов противодействия беспилотным летательным аппаратам. Ч. 1. Беспилотный летательный аппарат как объект обнаружения и поражения // Системы управления, связи и безопасности. 2020. № 1. С. 109-146. DOI: 10.24411/2410-9916-2020-10105.

Kartashov V.M., Oleynikov V.N, Sheyko S.A., Koryttsev I.V., Babkin S.I., Zubkov O.V. Peculiarities of small unmanned aerial vehicles detection and recognition // Telecommunications and Radio Engineering. 2019. Vol. 78, Issue 9. Р. 771-781.

Oleynikov V. N , Zubkov O. V., Kartashov V. M., Korytsev I. V., Babkin S. I., Sheiko S. A. Investigation of detection and recognition efficiency of small unmanned aerial vehicles on their acoustic emission // Telecommunications and Radio Engineering. 2019. Vol. 78, Issue 9. Р. 759-770.

Тихонов В.А., Карташов В.М., Олейников В.М., Леонидов В.И., Тимошенко Л.П., Селезнев И.С., Рыб-ников Н.В.. Обнаружение-распознавание беспилотных летательных аппаратов с использованием составной модели авторегрессии их акустического излучения // Вісник НТУУ «КПІ». Радіотехніка. Радіоапаратобудування. Вип. №81, 2020; С. 38–46. DOI: https://doi.org/10.20535/RADAP.2020.81.38-46.

Ситнік О.В., Карташов В.М. Радіотехнічні системи : навч. посібник. Харків : Сміт, 2009. 448 с.

Омельченко В.А., Безрук В.М., Коваленко Н.П. Распознавание заданных радиосигналов при наличии неизвестных сигналов на авторегрессионной основе // Радиотехника. 2001. № 123. С. 195–199.

Дробахин О.О. Автоматизация процесса распознавания сигналов дефектоскопа на основе модели линейного предсказания // Дефектоскопия. 1985. № 10. С. 64–67.

Рамишвили Г.С. Автоматическое распознавание говорящего по голосу. Москва : Радио и связь, 1981. 224 с.

Калистратова М.А., Кон А.И. Радиоакустическое зондирование атмосферы. Москва : Наука, 1985. 200 с.

Карташов В.М., Тихонов В.А., Олейников В.Н. Обработка сигналов в радиоэлектронных системах дистанционного мониторинга атмосферы. Харьков : ХНУРЭ, 2014. 312 с.

Марпл.-мл. С. Л. Цифровой спектральный анализ и его приложения. Москва : Мир, 1990. 584 с.

Бокс Дж., Дженкинс Г. Анализ временных рядов : пер. с. англ. Москва : Мир, 1974. Вып.1. 406 с.

Brockwell P.J., Davis R.A. Introduction to Time Series and Forecasting. Springer, 2002. Р. 434.

Кармалита В.А. Цифровая обработка случайных колебаний. Москва : Машиностроение, 1986. 80 с.

Downloads

Published

2022-06-24

How to Cite

Tikhonov, V. ., Kartashov, V. ., & Kartashov, O. . (2022). Model for estimating statistical characteristics of the pre-stroke warehouse process based on average monthly temperatures analysis. Radiotekhnika, 2(209), 239–245. https://doi.org/10.30837/rt.2022.2.209.24

Issue

No. 209 (2022): Radiotekhnika

Section

Articles

License

Authors who publish with this journal agree to the following terms:

1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).