Swarm intention identification via dynamic distribution probability image

作     者：Yinhan WANG Jiang WANG Shaoming HE Fei WANG Qi WANG Yinhan WANG;Jiang WANG;Shaoming HE;Fei WANG;Qi WANG

作者机构：School of Aerospace EngineeringBeijing Institute of TechnologyBeijing 100081China Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2024年第37卷第10期

页      面：380-392页

核心收录：

学科分类：11[军事学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 1109[军事学-军事装备学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Natural Science Foundation of China(No.52302449) 

主　　题：Intention Identification Kalman filters Convolutional neural networks Swarm intelligence 

摘      要：In the realm of decision-making for defense and security applications,it is paramount to swiftly and accurately identify the intentions of incoming *** identification methods predominantly focus on single-target applications and overlook the perturbations introduced by measurement *** this study,we propose a novel concept:the Dynamic Distribution Probability(DDP)image,which is constructed using the estimated state and its covariance *** grayscale pixel value within the image signifies the probability of the presence of the agent within the *** proposed identification scheme integrates the use of Extended Kalman Filter(EKF),Convolutional Neural Network(CNN),Back Propagation(BP)network,and Gated Recurrent Unit(GRU)***,the DDP image is processed through a CNN to distill the formation characteristics,and the estimated swarm state from EKF is inputted into a BP network to deduce the kinematic *** outputs from both networks are summed and subsequently channeled into a GRU network to capture temporal *** numerical simulations and flight experiments are presented to demonstrate the robust anti-noise capability of the proposed scheme compared with conventional methods,as well as its superior training efficiency.