The purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural network (BP-ANN) algorithm, with the objective of optimizing the structure of backpropagation network, to increase the efficiency and decrease the recognition time. A hardware-in-the-loop system for UAV target automatic recognition is also developed.
The hybrid model of BP-ANN structure is established for aircraft automatic target recognition. This proposed method identifies controller parameters and reduces the computational complexity. Approaching speed of the network is faster and recognition accuracy is higher. This kind of network combines or better fuses the advantages of backpropagation artificial neural algorithm and Hu moment. with advantages of two networks and improves the speed and accuracy of identification. Finally, a hardware-in-the-loop system for UAV target automatic recognition is also developed.
The double hidden level backpropagation artificial neural can easily increase the speed of recognition process and get a good performance for recognition accuracy.
The proposed backpropagation artificial neural algorithm can be ANN easily applied to practice and can help the design of the aircraft automatic target recognition system. The standard backpropagation algorithm has some obvious drawbacks, namely, converging slowly and falling into the local minimum point easily. In this paper, an improved algorithm based on the standard backpropagation algorithm is constructed to make the aircraft target recognition more practicable.
A double hidden levels backpropagation artificial neural algorithm is presented for automatic target recognition system of UAV.
This work is supported by National Training Programs of Innovation and Entrepreneurship for Undergraduates under grant #201510006022 and Aeronautical Foundation of China under grant #20135851042.
Jia, J. and Duan, H. (2017), "Automatic target recognition system for unmanned aerial vehicle via backpropagation artificial neural network", Aircraft Engineering and Aerospace Technology, Vol. 89 No. 1, pp. 145-154. https://doi.org/10.1108/AEAT-07-2015-0171Download as .RIS
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