Wang Peng, Yang Mei, Peng Yong, Zhu Jiancheng, Ju Rusheng, Yin Quanjun
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.
Entropy (Basel). 2019 Aug 6;21(8):767. doi: 10.3390/e21080767.
Since the submarine has become the major threat to maritime security, there is an urgent need to find a more efficient method of anti-submarine warfare (ASW). The digital twin theory is one of the most outstanding information technologies, and has been quite popular in recent years. The most influential change produced by digital twin is the ability to enable real-time dynamic interactions between the simulation world and the real world. Digital twin can be regarded as a paradigm by means of which selected online measurements are dynamically assimilated into the simulation world, with the running simulation model guiding the real world adaptively in reverse. By combining digital twin theory and random finite sets (RFSs) closely, a new framework of sensor control in ASW is proposed. Two key algorithms are proposed for supporting the digital twin-based framework. First, the RFS-based data-assimilation algorithm is proposed for online assimilating the sequence of real-time measurements with detection uncertainty, data association uncertainty, noise, and clutters. Second, the computation of the reward function by using the results of the proposed data-assimilation algorithm is introduced to find the optimal control action. The results of three groups of experiments successfully verify the feasibility and effectiveness of the proposed approach.
由于潜艇已成为海上安全的主要威胁，迫切需要找到一种更有效的反潜战（ASW）方法。数字孪生理论是最杰出的信息技术之一，近年来颇受欢迎。数字孪生产生的最具影响力的变化是能够实现模拟世界与现实世界之间的实时动态交互。数字孪生可以被视为一种范式，通过它将选定的在线测量值动态地融入模拟世界，同时运行的模拟模型反向自适应地引导现实世界。通过将数字孪生理论与随机有限集（RFSs）紧密结合，提出了一种新的反潜战传感器控制框架。提出了两种关键算法来支持基于数字孪生的框架。首先，提出了基于RFS的数据同化算法，用于在线同化具有检测不确定性、数据关联不确定性、噪声和杂波的实时测量序列。其次，引入利用所提出的数据同化算法的结果计算奖励函数，以找到最优控制动作。三组实验结果成功验证了所提方法的可行性和有效性。
