Abstract
The digital twin is becoming the most promising emerging technology in the field of unmanned combat and has the potential to innovate future combat styles. Online battlefield learning is one of the key technologies for supporting the successful application of digital twin in unmanned combat. Since there is an urgent need for effective algorithms for online learning the battlefield states in real time, a new random finite set- (RFS-) based algorithm is proposed in the presence of detection uncertainty including clutters, missed detection, and noises. The system architecture and operational mode for implementing the digital twin-enabled online battlefield learning are provided. The unmanned ground vehicle (UGV) is employed as the experimental subject for systematically describing the proposed algorithm. The system architecture for implementing the digital twin-enabled online battlefield learning is firstly given, and its operational mode is also described in detail. The RFS-based digital twin models including the battlefield state model, UGV motion model, and sensor model are designed. The Bayesian inference is adopted, and the probability hypothesis density (PHD) filter is modified to implement the online learning process. At last, a group of experiments are conducted to verify the performance and effectiveness of the proposed algorithm. The research work in this paper will provide a good demonstration of the application of digital twin in unmanned combat.
Highlights
Battlefield learning means sensing the entities on the battlefield rapidly, understanding the current situation comprehensively, and predicting future status accurately
We propose employing the random set theory to overcome these disadvantages. e proposed random finite set- (RFS-)based algorithm can overcome the limitations of conventional algorithms very well, because it takes into account a more realistic situation where the randomly varying number of targets and measurements, detection uncertainty, false alarms, and association uncertainty are all taken into consideration
(1) for i 1 to N do Generate the particles for the unmanned ground vehicle (UGV) state, X1 ∼ f(X1|X(ki−)1, Uk−1); (3) Get predicted battlefield probability hypothesis density (PHD) through the predict step of PHD filter; (4) Get updated battlefield PHD through the update step of PHD filter; (5)
Summary
Battlefield learning means sensing the entities on the battlefield rapidly, understanding the current situation comprehensively, and predicting future status accurately. The digital twin is a system composed of physical objects, simulation models, and the real-time dynamic interaction between them It requires building the simulation models for real entities and simulating their behaviors [22]. With the help of various high-performance sensors and high-speed communication technologies, the digital twin can present and predict the actual situation of physical entities in near real time by integrating the data of physical entities. It enhances the ability of analysis and simulation and controls the physical entities through the virtual-real interactive interfaces and data fusion algorithms [23]. Key to enable digital twin in unmanned combat is understanding the evolving situations in the battlefield accurately and timely
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