A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System
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 cra...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/7190446 |
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| _version_ | 1832557101536247808 |
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| author | Min Huang Dandan Liu Liyun Ma Jingyang Wang Yuming Wang Yazhou Chen |
| author_facet | Min Huang Dandan Liu Liyun Ma Jingyang Wang Yuming Wang Yazhou Chen |
| author_sort | Min Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6cdc90cc314f4d7ca3b68608844dba29 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-6cdc90cc314f4d7ca3b68608844dba292025-02-03T05:43:34ZengWileyComplexity1099-05262021-01-01202110.1155/2021/7190446A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection SystemMin Huang0Dandan Liu1Liyun Ma2Jingyang Wang3Yuming Wang4Yazhou Chen5National Key Laboratory on Electromagnetic Environment EffectsSchool of Information Science and EngineeringNational Key Laboratory on Electromagnetic Environment EffectsSchool of Information Science and EngineeringNational Key Laboratory on Electromagnetic Environment EffectsNational Key Laboratory on Electromagnetic Environment EffectsWith 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.http://dx.doi.org/10.1155/2021/7190446 |
| spellingShingle | Min Huang Dandan Liu Liyun Ma Jingyang Wang Yuming Wang Yazhou Chen A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System Complexity |
| title | A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System |
| title_full | A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System |
| title_fullStr | A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System |
| title_full_unstemmed | A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System |
| title_short | A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System |
| title_sort | prediction method of electromagnetic environment effects for uav lidar detection system |
| url | http://dx.doi.org/10.1155/2021/7190446 |
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