Automatic target recognition system for unmanned aerial vehicle via backpropagation artificial neural network. Issue 1 (3rd January 2017)

• Record Type: Journal Article
• Authors: Jia, Jiaqi; Duan, Haibin
• Is Part Of: Aircraft engineering and aerospace technology. Volume 89:Issue 1(2017)
• Page Start: 145
• Page End: 154
• Publication Date: 2017-01-03
• Subjects: Artificial neural network (ANN) -- Automatic target recognition (ATR) -- Backpropagation (BP) -- Hu moment -- Unmanned aerial vehicle (UAV); Aerospace engineering -- Periodicals; Aeronautics -- Systems engineering -- Periodicals; Astronautics -- Systems engineering -- Periodicals; Airplanes -- Equipment and supplies -- Periodicals; Space vehicles -- Equipment and supplies -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1108/AEAT-07-2015-0171
• Languages: English
• ISSNs: 1748-8842

Abstract

Abstract : Purpose: 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. Design/methodology/approach: 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. Findings: The double hidden level backpropagation artificial neural can easily increase the speed of recognition process and get a good performance for recognition accuracy. Research limitations/implications: 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. Originality/value: A double hidden levels backpropagation artificial neural algorithm is presented for automatic target recognition system of UAV.