Swarm intention identification via dynamic distribution probability image  

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

机构地区：[1]School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China [2]Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China

出　　处：《Chinese Journal of Aeronautics》2024年第10期380-392,共13页中国航空学报（英文版）

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

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 swarms.Conventional identification methods predominantly focus on single-target applications and overlook the perturbations introduced by measurement noise.In this study,we propose a novel concept:the Dynamic Distribution Probability(DDP)image,which is constructed using the estimated state and its covariance matrix.Each grayscale pixel value within the image signifies the probability of the presence of the agent within the swarm.Our proposed identification scheme integrates the use of Extended Kalman Filter(EKF),Convolutional Neural Network(CNN),Back Propagation(BP)network,and Gated Recurrent Unit(GRU)network.Specifically,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 information.The outputs from both networks are summed and subsequently channeled into a GRU network to capture temporal dynamics.Extensive 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.

关 键 词：INTENTION IDENTIFICATION Kalman filters Convolutional neural networks Swarm intelligence 

分 类 号：E91[军事] TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]