Abstract
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.
Highlights
Submarines are the main combat forces of modern maritime warfare, and the major threats to maritime security
Digital twin can assist in ensuring information continuity throughout the whole operation, sensor control, and system behavior predictions in anti-submarine warfare (ASW) based on simulations
Since there are two constituent objects in digital twin, in this paper, we propose two corresponding technologies to support the implementation of the digital twin-based framework: one is the random finite sets (RFSs)-based data-assimilation algorithm for assimilating real-time measurements into the running simulation model, and the other one is the computation of the reward function by using the results of the proposed data-assimilation algorithm for finding the optimal control action
Summary
Submarines are the main combat forces of modern maritime warfare, and the major threats to maritime security. Anti-submarine warfare (ASW) is a type of warfare that depends on surface warships, aircraft, or submarines to fight against enemy submarines. The key of ASW is to quickly identify and localize as many enemy submarines as possible. Sensor control is the key technology for the victory in ASW, so we focus on the innovation of the online sensor control method. Many works have been done to apply simulation-based approaches in naval warfare research, but there are quite a few effective methods for combining simulation technologies with the real ASW in real time. We study how to control the sensor of anti-submarine ships in ASW by employing simulation theory, random finite set (RFS) theory, and digital twin theory
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