An Intelligent Mission Planning Model for the Air Strike Operations against Islands Based on Neural Network and Simulation

Zhihua Song,Fa Zhang,Yongmei Zhao,Tao Dong,Han Zhang,Lianbo Ma

doi:10.1155/2022/8172907

Zhihua Song, Fa Zhang + Show 4 more

Open Access

https://doi.org/10.1155/2022/8172907

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Abstract

Mission planning of air strike operations is hard because it has to give instructions to a large number of units during a relatively long period of time in an uncertain environment. If some instruction parameters can be calculated by an intelligent agent, better strategies can be found more quickly. In a specific combat scenario of air strike operations against islands, an intelligent model is proposed to improve the performance and flexibility of mission planning. The proposed intelligent mission planning model is based on rule-based decision and uses a fully connected recurrent neural network to calculate some of the decision parameters. The proposed intelligent mission planning model shows better results as compared to rule-based decision making with randomized parameters, and it performs as good as experts in the test set of the specific combat scenario.

Highlights

Air strike forces composed of fighter, bomber, early warning aircraft, electronic countermeasure (ECM) aircraft, and unmanned aerial vehicle provide a versatile striking force capable of rapid and distant employment. ese forces can quickly gain and sustain air superiority over regional aggressors, permitting rapid air attacks on air and surface targets
We focus on a specific scenario of air attack operations against islands and try to use an intelligent agent to make some of the decisions, so as to improve the efficiency and speed of mission planning
Mission planning of air strike operations is hard because its decision space is huge and it is difficult or even impossible to design a general mission planning model

Summary

Introduction

Air strike forces composed of fighter, bomber, early warning aircraft, electronic countermeasure (ECM) aircraft, and unmanned aerial vehicle provide a versatile striking force capable of rapid and distant employment. ese forces can quickly gain and sustain air superiority over regional aggressors, permitting rapid air attacks on air and surface targets. We focus on a specific scenario of air attack operations against islands and try to use an intelligent agent to make some of the decisions, so as to improve the efficiency and speed of mission planning. Combat-level instructions include mission parameters such as the patrol area of early warning aircraft, the attack position and formation relationship of bombers, the formation relationship and patrol airspace of fighters, the formation and interference position of the ECM aircraft, responsible sector of ground-to-air missiles, and so on. Action-level decisions include flight route to specified position and tasks performed automatically. e automatic task of the early warning aircraft is to detect as the instructions indicate, the automatic task of the bomber is to find and attack the target within its scope, the automatic task of the fighter is to attack the enemy fighter found, the automatic task of the ground radar is to detect the air target, and the automatic task of the ground-to-air missile is to automatically strike the target that meets the shooting conditions

Intelligent Agent Based on Neural Network

Experimental Results

Conclusion