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İnsansız Hava Araçlarında Angajmandan Kaçış Yolu Kestirimi İçin Çok Sensörlü Veri Füzyonu

Yıl 2021, Sayı: 32, 705 - 710, 31.12.2021
https://doi.org/10.31590/ejosat.1039358

Öz

Savaşta başarı elde etmenin en önemli koşullarından birisi, hava üstünlüğünü sağlamaktır. Saldırgan muharebe ortamında bulunan bir savaş uçağının, gereken hayatta kalma özelliklerine sahip olması gerekmektedir. İnsansız hava araçlarında (İHA), kontrol istasyonuyla olan bağlantının kesilmesi durumunda, İHA’nın hareket ve seyrüsefer kabiliyetlerini koruması zorlaşır. Bu bildiride, insansız hava araçlarının angajmandan kaçışını sağlamak için çok sensörlü veri füzyonu yöntemiyle geliştirilen bir kaçış yolu kestirimi algoritması sunulmaktadır. Gelen radar verileri, tahmin yapmak üzere Genişletilmiş Kalman Filtresine sokularak değerlendirilir. Yapılan tahminler, doğrusal olmayan programlama yönteminde kullanılır ve anlık optimal kaçış yolu belirlenir. Sahip olunan kısıtlamalar ve amaç fonksiyonu lineer olmadığı için kısıtlı optimizasyon yöntemi olarak doğrusal olmayan programlama kullanılır. Simülasyon sonuçlarına göre, önerilen yöntem seçilen senaryoda angajmandan kaçış için umut verici sonuçlar sunmuştur.

Kaynakça

  • Burgin, G. H., & Owens, A. J. (1975). An adaptive maneuvering logic computer program for the simulation of one-to-one air-to-air combat. Volume 2: Program description
  • Dupuy, T. N. (1987). Understanding War: History and Theory of Combat. Paragon House.
  • Cappello, F., Sabatini, R., Ramasamy, S., & Marino, M. (2015). Particle filter based multi-sensor data fusion techniques for RPAS navigation and guidance. 2015 IEEE Metrology for Aerospace (MetroAeroSpace). Published. https://doi.org/10.1109/metroaerospace.2015.7180689
  • Find minimum of constrained nonlinear multivariable function - MATLAB fmincon. (2021). MATLAB & Simulink. https://www.mathworks.com/help/optim/ug/fmincon.html
  • Introduction to Kalman Filter and Its Applications website. (2021). Mathworks.https://www.mathworks.com/matlabcentral/fileexchange/68262-introduction-to-kalman-filter-and-its-applications
  • López, N.R., & ŻBikowski, R. (2018). Effectiveness of Autonomous Decision Making for Unmanned Combat Aerial Vehicles in Dogfight Engagements. Journal of Guidance, Control, and Dynamics, 41(4), 1021–1024. https://doi.org/10.2514/1.g002937
  • McGrew, J. S., How, J. P., Williams, B., & Roy, N. (2010). Air-combat strategy using approximate dynamic programming. Journal of guidance, control, and dynamics, 33(5), 1641-1654.
  • MathWorks website. (2021). https://www.mathworks.com/
  • Meinhold, R. J., & Singpurwalla, N. D. (1983). Understanding the Kalman Filter. The American Statistician, 37(2), 123–127. https://doi.org/10.1080/00031305.1983.10482723
  • Kim, Y., & Bang, H. (2019). Introduction to Kalman Filter and Its Applications. Introduction and Implementations of the Kalman Filter. Published. https://doi.org/10.5772/intechopen.80600
  • Neff, M., Expressing Points in Different Coordinate Systems, [Online], http://www.dgp.toronto.edu/~neff/teaching/418/transformations/transformation.html, Access Date: 26 Nov. 2021.
  • Nonlinear Programming. (2021). MATLAB & Simulink. https://www.mathworks.com/discovery/nonlinear-programming.html
  • Ribeiro, M. I. (2004). Kalman and extended kalman filters: Concept, derivation and properties. Institute for Systems and Robotics, 43, 46.

Multi-Sensor Data Fusion for Path Prediction of Escaping from Engagement in Unmanned Aerial Vehicle Scenario

Yıl 2021, Sayı: 32, 705 - 710, 31.12.2021
https://doi.org/10.31590/ejosat.1039358

Öz

Achieving air superiority is one of the key steps to success in warfare. It is necessary for a combat aircraft to have the survivability it needs in an aggressive combat environment. Unmanned aerial vehicles (UAVs) suffer from maintaining the maneuverability and navigational ability in the event of a disconnection from the control station. In this paper, an escape path prediction algorithm developed by fusing multi-sensor data is presented to facilitate the escape of engagement of UAVs. Data from radars are evaluated in the Extended Kalman Filter and used to make estimations. The estimations made are used in constraint optimization to generate an instantaneous optimal escape route. Since the constraints and objective function are not linear, nonlinear programming is used as a method of constraint optimization. According to the simulation results, the proposed method shows a promising result for escaping from engagement in the selected scenario.

Kaynakça

  • Burgin, G. H., & Owens, A. J. (1975). An adaptive maneuvering logic computer program for the simulation of one-to-one air-to-air combat. Volume 2: Program description
  • Dupuy, T. N. (1987). Understanding War: History and Theory of Combat. Paragon House.
  • Cappello, F., Sabatini, R., Ramasamy, S., & Marino, M. (2015). Particle filter based multi-sensor data fusion techniques for RPAS navigation and guidance. 2015 IEEE Metrology for Aerospace (MetroAeroSpace). Published. https://doi.org/10.1109/metroaerospace.2015.7180689
  • Find minimum of constrained nonlinear multivariable function - MATLAB fmincon. (2021). MATLAB & Simulink. https://www.mathworks.com/help/optim/ug/fmincon.html
  • Introduction to Kalman Filter and Its Applications website. (2021). Mathworks.https://www.mathworks.com/matlabcentral/fileexchange/68262-introduction-to-kalman-filter-and-its-applications
  • López, N.R., & ŻBikowski, R. (2018). Effectiveness of Autonomous Decision Making for Unmanned Combat Aerial Vehicles in Dogfight Engagements. Journal of Guidance, Control, and Dynamics, 41(4), 1021–1024. https://doi.org/10.2514/1.g002937
  • McGrew, J. S., How, J. P., Williams, B., & Roy, N. (2010). Air-combat strategy using approximate dynamic programming. Journal of guidance, control, and dynamics, 33(5), 1641-1654.
  • MathWorks website. (2021). https://www.mathworks.com/
  • Meinhold, R. J., & Singpurwalla, N. D. (1983). Understanding the Kalman Filter. The American Statistician, 37(2), 123–127. https://doi.org/10.1080/00031305.1983.10482723
  • Kim, Y., & Bang, H. (2019). Introduction to Kalman Filter and Its Applications. Introduction and Implementations of the Kalman Filter. Published. https://doi.org/10.5772/intechopen.80600
  • Neff, M., Expressing Points in Different Coordinate Systems, [Online], http://www.dgp.toronto.edu/~neff/teaching/418/transformations/transformation.html, Access Date: 26 Nov. 2021.
  • Nonlinear Programming. (2021). MATLAB & Simulink. https://www.mathworks.com/discovery/nonlinear-programming.html
  • Ribeiro, M. I. (2004). Kalman and extended kalman filters: Concept, derivation and properties. Institute for Systems and Robotics, 43, 46.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Enver Nurullah Gökal 0000-0003-3237-8633

Ufuk Sakarya 0000-0002-8365-3415

Yayımlanma Tarihi 31 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 32

Kaynak Göster

APA Gökal, E. N., & Sakarya, U. (2021). İnsansız Hava Araçlarında Angajmandan Kaçış Yolu Kestirimi İçin Çok Sensörlü Veri Füzyonu. Avrupa Bilim Ve Teknoloji Dergisi(32), 705-710. https://doi.org/10.31590/ejosat.1039358