Battlefield Situation Information Research Articles

Implementation of Tactical Decision Aids Based on Event Knowledge Graph

This paper proposes a new tactical decision aids method based on event knowledge graph (EventKG). In the warfare domain, EventKG can be constructed through event types design, event network construction and transition probability computation between events. Initially, four event classes are introduced in accordance with the OODA loop, and eighteen subclasses are further decomposed. With the aids of a common event template, all the events taking place in the battle field can be described. Event networks are built by adopting the hierarchical task network (HTN) and described through Bayesian network, to exhibit various relations between battle events. Transition probability, namely the occurrence probability of next possible event, is computed by using the prior probability and conditional probability of event occurring. On the basis of structured EventKG, entity knowledge graph (EKG) and entity relation knowledge graph (ERKG), tactical decision aid instructions can be generated by combining with the battlefield situation information.

Task-oriented Battlefield Situation Information Hybrid Recommendation Model

In the process of interaction between users and battlefield situation information, combat tasks are the key factors that affect users’ information selection. In order to solve the problems of battlefield situation information recommendation (BSIR) for combat tasks, we propose a task-oriented battlefield situation information hybrid recommendation model (TBSI-HRM) based on tensor factorization and deep learning. In the model, in order to achieve high-precision personalized recommendations, we use Tensor Factorization (TF) to extract correlation relations and features from historical interaction data, and use Deep Neural Network (DNN) to learn hidden feature vectors of users, battlefield situation information and combat tasks from auxiliary information. The results are predicted through logistic regression. To solve the multi-source heterogeneity of battlefield situation information, we design a hybrid learning and presentation model that integrates multiple deep learning models such as Doc2Vec, fully connected network and convolutional neural network (CNN), to integrate the rich and diverse data information in situational awareness system effectively. We perform experiments with the maneuvers dataset to test and evaluate the model through scenario simulation.

A Command and Control System for Air Defense Forces with Augmented Reality and Multimodal Interaction

This work proposes a framework for the command and control system of air defense forces based on augmented reality technology. The system uses Microsoft HoloLens hardware as a carrier to realize the holographic display of battlefield situation information. The system can help the commander to perceive and control the situation on the battlefield in an efficient manner, in order to reduce the decision-making time of the commander. Commanders can efficiently interact with the system through gestures and voice, deploy simulated military units in the system through gestures, and send commands to weapon systems through the system. At the same time, the system also supports multi-person real-time collaborative operation, which enables the joint decision-making and sharing of commanders. This system is of great significance to improve operational efficiency and reliability, and at the same time provides an open platform for operational process planning, deduction, simulation, and verification evaluation.

Battlefield Situation Information Recommendation Based on Recall-Ranking

With the rapid development of information technology, battlefield situation data presents the characteristics of “4V” such as Volume, Variety, Value and Velocity. While enhancing situational awareness, it also brings many challenges to battlefield situation information recommendation (BSIR), such as big data volume, high timeliness, implicit feedback and no negative feedback. Focusing on the challenges faced by BSIR, we propose a two-stage BSIR model based on deep neural network (DNN). The model utilizes DNN to extract the nonlinear relationship between the data features effectively, mine the potential content features, and then improves the accuracy of recommendation. These two stages are the recall stage and the ranking stage. In the recall stage, the candidate set of situation information is generated, and the massive situation information is reduced to a small candidate subset that is highly relevant to the commanders. In the ranking stage, the situation information in the candidate set is accurately scored and then sorted, and the situation information with a high score is eventually recommended to the commanders. Finally, we use the historical date from the situation management of real combat training information systems as the dataset and verify the effective-ness of the algorithm through experiments, and analyze the effects of the depth and width of the neural network on the performance of the algorithm.

A Heterogeneous Battlefield Situation Information Sharing Method Based on Content

Various information systems adopt different information category standards and description methods even for the same battlefield space. This leads to the heterogeneity of information distributed in different systems, which hinders the information sharing among different systems. In this paper, we adopt the idea of schema mapping and design a framework of realizing heterogeneous information sharing based on content. We design a concept-logic tree model to organize the battlefield situation information and realize the mapping from local concept models in different systems to a global unified concept model. By constructing a unified information space, we realize the centralized organization, storage, and management of entity description information. Then, the information broadcasting mechanism and content based information query mechanism are designed to realize information sharing. Theoretical analysis and experiment results verify the effectiveness of the proposed framework.

Online Command and Guidance Strategy Generation Method Based on Rolling Time Domain Control

In order to generate the command and guidance tactics path based on the current battlefield situation information in real time, a method of online generation of command and guidance strategy based on rolling time domain control is proposed. Firstly, the optimal control model of command and guidance strategy is established. Then the rolling time domain control method is applied to the command and guidance strategy generation model, and conducted a solution study. Finally, a simulation study was conducted on a PC, The simulation results show that the on – line command and guidance strategy generation method based on rolling time domain control is reasonable and effective, which is in line with the actual situation of air combat.

Research on the Effect of Information Sharing on Command Decision-Making in Network-Centric Warfare

In network centric warfare, battlefield information sharing can provide more accurate and timely battlefield situation for combat decision making. This paper analyzes the influence of different levels of battlefield information sharing on combat decision making. Bayesian criterion is used to quantify the state change of decision-making conditions on battlefield situation information, and the results are expressed in the form of information entropy. This method can be used to evaluate the influence of battlefield information sharing on combat decision making conditions.

Target threat level assessment based on cloud model under fuzzy and uncertain conditions in air combat simulation

The good real-time performance and the accuracy of the target threat level assessment are very important in air combat simulation. In order to solve the problem of target threat level assessment caused by the fuzziness and uncertainty of battlefield information collection, the target threat assessment technology based on cloud model is proposed. Using fuzzy description, battlefield situation is expressed in many qualitative concepts, and then these qualitative concepts are expressed and processed using cloud model related theory, and through Bayes revision on the membership clouds obtained depending on experts' experience which evaluated qualitative concepts, the staff gauges clouds were obtained. Finally the cloud diagram of target threat assessment was obtained through battlefield situation information matching the staff gauges clouds, and threat level assessment was realized. The description of battlefield situation with qualitative concept and membership clouds achieve a good integration in fuzziness and uncertainty, which reflect fuzziness and uncertainty when obtain information in air combat. Qualitative and quantitative concepts descriptions do not need precision battlefield situation data in target threat level assessment using cloud model. An example was given to validate the target threat level assessment technology based on cloud model, and the results indicate that threat level assessment can be realized availably by this technology.

Method for Constructing Three-Dimensional Display Model on Battlefield Situation Awareness

Using three-dimensional graphic technology to construct virtual battlefield, realize battlefield situation information visualization, enhance situation awareness fidelity and immersion, and improve battlefield situation awareness naturalness, is an important orientation of C4ISR (Command, Control, Communication, Computer, Intelligence, Surveillance and Reconnaissance) system. For normal three-dimensional model couldn't describe the dynamic changing of virtual scene, we put forward a method constructing and drawing flow of three-dimensional model based on the data model of application domain for large-scale virtual scenes. We validate the feasibility of the method combined with the radar power bubble on electronic jam model.