Technology challenges of stealth unmanned combat aerial vehicles

The design of aircraft has evolved over time from the classical design approach to the more modern computer based design method utilising multivariate design optimisation. In recent years aircraft concepts and configurations have become more diverse and complex thus pushing many synthesis packages beyond their capability. Furthermore, many examples of aircraft design software focus on the analysis of one particular concept thus requiring separate packages for each concept. This can lead to complications in comparing concepts and configurations as differences in performance may originate from different prediction toolsets being used. This paper presents the GENUS Aircraft Design Framework developed by Cranfield University's Aircraft Design Group to address these issues. The paper reviews available aircraft design methodologies and describes the challenges faced in their development and application. Following this, the GENUS aircraft design environment is introduced, along with the theoretical background and practical reasoning behind the program architecture. Particular attention is given to the programming, choice of methodology and optimization techniques involved. Subsequently, some applications of the developed methodology, implemented in the framework are presented to illustrate the diversity of the approach. Three special classes of aircraft design concept are presented briefly.

show abstract

“…Subsequent to these founding studies, GENUS is being applied to supersonic business jets 54 and unmanned combat air vehicles. 55…”

Section: The Genus Aircraft Conceptual Design Environmentmentioning

confidence: 99%

“…Subsequent to these founding studies, GENUS is being applied to supersonic business jets 54 and unmanned combat air vehicles. 55 The GENUS software tool's main task is to implement the Design framework, the structure of which is shown in Figure 1.…”

Section: The Genus Aircraft Conceptual Design Environmentmentioning

confidence: 99%

The GENUS aircraft conceptual design environment

Smith

Sziroczák

Abbe

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

show abstract

“…UCAVs are widely used in the future of air combat. Combined with advanced technologies, future UCAVs will be able to operate in highly contested airspace in pre-emptive and reactive roles such as suppression and destruction of enemy air defences, penetrating surveillance, and strike of high-value targets [5,6]. Furthermore, future capabilities are likely to extend on the operational speeds and maneuverability requirements to include supersonic regimes [7,8].…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning and its application in autonomous fitting optimization for attack areas of UCAVs

Qiu

Liu

et al. 2020

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

The ever-changing battlefield environment requires the use of robust and adaptive technologies integrated into a reliable platform. Unmanned combat aerial vehicles (UCAVs) aim to integrate such advanced technologies while increasing the tactical capabilities of combat aircraft. As a research object, common UCAV uses the neural network fitting strategy to obtain values of attack areas. However, this simple strategy cannot cope with complex environmental changes and autonomously optimize decision-making problems. To solve the problem, this paper proposes a new deep deterministic policy gradient (DDPG) strategy based on deep reinforcement learning for the attack area fitting of UCAVs in the future battlefield. Simulation results show that the autonomy and environmental adaptability of UCAVs in the future battlefield will be improved based on the new DDPG algorithm and the training process converges quickly. We can obtain the optimal values of attack areas in real time during the whole flight with the well-trained deep network.

show abstract

“…Meanwhile, the survivability and combat effectiveness of stealth UAV will have a great influence on modern air warfare [1,2]. The nose, fuselage, and tail of stealth UAV have relatively small radar crosssection (RCS), which aims to reduce the detection probability of radar system and improve the penetration ability and survivability of the stealth UAV [3]. However, previous works have mainly concentrated on the path planning in the static environment, there are many theoretical challenges and practical problems for stealth UAV on the problem of penetration path planning, such as the constraint of attitude angle and control of track point, the efficiency of path planning algorithm, and real-time performance.…”

Section: Introductionmentioning

confidence: 99%

Rapid Penetration Path Planning Method for Stealth UAV in Complex Environment with BB Threats

Zhang

Dai

et al. 2020

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

This paper presents the flight penetration path planning algorithm in a complex environment with Bogie or Bandit (BB) threats for stealth unmanned aerial vehicle (UAV). The emergence of rigorous air defense radar net necessitates efficient flight path planning and replanning for stealth UAV concerning survivability and penetration ability. We propose the improved A-Star algorithm based on the multiple step search approach to deal with this uprising problem. The objective is to achieve rapid penetration path planning for stealth UAV in a complex environment. Firstly, the combination of single-base radar, dual-base radar, and BB threats is adopted to different threat scenarios which are closer to the real combat environment. Besides, the multistep search strategy, the prediction technique, and path planning algorithm are developed for stealth UAV to deal with BB threats and achieve the penetration path replanning in complex scenarios. Moreover, the attitude angle information is integrated into the flight path which can meet real flight requirements for stealth UAV. The theoretical analysis and numerical results prove the validity of our method.

show abstract

Technology challenges of stealth unmanned combat aerial vehicles

Cited by 21 publications

References 119 publications

The GENUS aircraft conceptual design environment

The GENUS aircraft conceptual design environment

Deep reinforcement learning and its application in autonomous fitting optimization for attack areas of UCAVs

Rapid Penetration Path Planning Method for Stealth UAV in Complex Environment with BB Threats

Contact Info

Product

Resources

About