A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System

Min Huang,Yuming Wang,Yazhou Chen,Liyun Ma,Dandan Liu,Jingyang Wang,Min Xia

doi:10.1155/2021/7190446

Abstract

With the rapid development of science and technology, UAVs (Unmanned Aerial Vehicles) have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm (XGB), Gradient Boosting Decision Tree algorithm (GBDT), K Nearest Neighbor algorithm (KNN), and Decision Tree algorithm (DT). Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield.

Highlights

Modern warfares are information and electronic warfare
Many enemies and our radars are deployed on the battlefield, coupled with natural electromagnetic radiation and man-made electromagnetic radiation interference, making the electromagnetic environment of the battlefield more complicated [1]. e UAV LiDAR detection system plays an important role in informatization electronic warfare operations. e complex electromagnetic environment has caused serious interference to the UAV LiDAR detection system, threatening the safety and combat effectiveness of the UAV [2]
When the UAV LiDAR detection system is subjected to electromagnetic interference during the flight, to ensure safety, measures such as leaving the interference zone and returning home are generally taken, but it will have a great impact on the completion of the mission

Summary

Introduction

E complex electromagnetic environment has caused serious interference to the UAV LiDAR detection system, threatening the safety and combat effectiveness of the UAV [2]. E LiDAR detection system plays an important role in the flight safety of UAV. It is affected by the electromagnetic environment of the complex battlefield, which makes the UAV LiDAR detection system have detection errors, affects the construction of point cloud maps, and causes inaccurate target detection. Research is done on the prediction method of the complex electromagnetic environment effect of the battlefield, so that the UAV LiDAR detection system can realize. Electromagnetic environment effect prediction is an artificial intelligence process to complete machine decision-making with the help of a large number of experimental data. We chose machine learning algorithms rather than deep learning to solve this problem

Methods

Results

Conclusion