TY - JOUR
T1 - An information fusion method based on deep learning and fuzzy discount-weighting for target intention recognition
AU - Zhang, Zhuo
AU - Wang, Hongfei
AU - Geng, Jie
AU - Jiang, Wen
AU - Deng, Xinyang
AU - Miao, Wang
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/3
Y1 - 2022/3
N2 - In the military confrontation environment, recognizing the target intention is helpful to know the target actions in advance, and the global intention recognition of target formations can provide decision-making reference for the command center. In this work, a new information fusion method for multi-target formation intention recognition is developed, which combines the advantages of deep learning and Dempster–Shafer theory. This method firstly construct the deep learning networks and design the corresponding conversion methods to obtain the uncertain information for target intention recognition. Then, a new fuzzy discount-weighting operation is proposed. This operation defines the new fuzzy discount rule and fuzzy weighting rule, which generates discount evidence and weighting coefficients to improve the reliability of evidence, then obtain a more reasonable fusion result. The simulation results show that the method is effective and feasible for global target intention recognition under uncertain and incomplete information.
AB - In the military confrontation environment, recognizing the target intention is helpful to know the target actions in advance, and the global intention recognition of target formations can provide decision-making reference for the command center. In this work, a new information fusion method for multi-target formation intention recognition is developed, which combines the advantages of deep learning and Dempster–Shafer theory. This method firstly construct the deep learning networks and design the corresponding conversion methods to obtain the uncertain information for target intention recognition. Then, a new fuzzy discount-weighting operation is proposed. This operation defines the new fuzzy discount rule and fuzzy weighting rule, which generates discount evidence and weighting coefficients to improve the reliability of evidence, then obtain a more reasonable fusion result. The simulation results show that the method is effective and feasible for global target intention recognition under uncertain and incomplete information.
KW - Deep learning
KW - Dempster–Shafer Theory
KW - Fuzzy discount
KW - Information fusion
KW - Target intention recognition
UR - http://www.scopus.com/inward/record.url?scp=85122595348&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2021.104610
DO - 10.1016/j.engappai.2021.104610
M3 - 文章
AN - SCOPUS:85122595348
SN - 0952-1976
VL - 109
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 104610
ER -