Improving UAV Remote Sensing Photogrammetry Accuracy Under Navigation Interference Using Anomaly Detection and Data Fusion
Accurate and robust navigation is fundamental to Unmanned Aerial Vehicle (UAV) remote sensing operations. However, the susceptibility of UAV navigation sensors to diverse interference and malicious attacks can severely degrade positioning accuracy and compromise mission integrity. Addressing these v...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2176 |
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| author | Chen Meng Haoyang Yang Cuicui Jiang Qinglei Hu Dongyu Li |
| author_facet | Chen Meng Haoyang Yang Cuicui Jiang Qinglei Hu Dongyu Li |
| author_sort | Chen Meng |
| collection | DOAJ |
| description | Accurate and robust navigation is fundamental to Unmanned Aerial Vehicle (UAV) remote sensing operations. However, the susceptibility of UAV navigation sensors to diverse interference and malicious attacks can severely degrade positioning accuracy and compromise mission integrity. Addressing these vulnerabilities, this paper presents an integrated framework combining sensor anomaly detection with a Dynamic Adaptive Extended Kalman Filter (DAEKF) and federated filtering algorithms to bolster navigation resilience and accuracy for UAV remote sensing. Specifically, mathematical models for prevalent UAV sensor attacks were established. The proposed framework employs adaptive thresholding and residual consistency checks for the real-time identification and isolation of anomalous sensor measurements. Based on these detection outcomes, the DAEKF dynamically adjusts its sensor fusion strategies and noise covariance matrices. To further enhance the fault tolerance, a federated filtering architecture was implemented, utilizing adaptively weighted sub-filters based on assessed trustworthiness to effectively isolate faults. The efficacy of this framework was validated through rigorous experiments that involved real-world flight data and software-defined radio (SDR)-based Global Positioning System (GPS) spoofing, alongside simulated attacks. The results demonstrate exceptional performance, where the average anomaly detection accuracy exceeded 99% and the precision surpassed 98%. Notably, when benchmarked against traditional methods, the proposed system reduced navigation errors by a factor of approximately 2-3 under attack scenarios, which substantially enhanced the operational stability of the UAVs in challenging environments. |
| format | Article |
| id | doaj-art-18a36f5cbe094236b8a76a5058d053bb |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-18a36f5cbe094236b8a76a5058d053bb2025-08-20T03:28:37ZengMDPI AGRemote Sensing2072-42922025-06-011713217610.3390/rs17132176Improving UAV Remote Sensing Photogrammetry Accuracy Under Navigation Interference Using Anomaly Detection and Data FusionChen Meng0Haoyang Yang1Cuicui Jiang2Qinglei Hu3Dongyu Li4School of Cyber Science and Technology, Beihang University, Beijing 100191, ChinaSchool of Engineering, University of Warwick, Coventry CV4 7AL, UKDepartment of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, ChinaSchool of Cyber Science and Technology, Beihang University, Beijing 100191, ChinaSchool of Cyber Science and Technology, Beihang University, Beijing 100191, ChinaAccurate and robust navigation is fundamental to Unmanned Aerial Vehicle (UAV) remote sensing operations. However, the susceptibility of UAV navigation sensors to diverse interference and malicious attacks can severely degrade positioning accuracy and compromise mission integrity. Addressing these vulnerabilities, this paper presents an integrated framework combining sensor anomaly detection with a Dynamic Adaptive Extended Kalman Filter (DAEKF) and federated filtering algorithms to bolster navigation resilience and accuracy for UAV remote sensing. Specifically, mathematical models for prevalent UAV sensor attacks were established. The proposed framework employs adaptive thresholding and residual consistency checks for the real-time identification and isolation of anomalous sensor measurements. Based on these detection outcomes, the DAEKF dynamically adjusts its sensor fusion strategies and noise covariance matrices. To further enhance the fault tolerance, a federated filtering architecture was implemented, utilizing adaptively weighted sub-filters based on assessed trustworthiness to effectively isolate faults. The efficacy of this framework was validated through rigorous experiments that involved real-world flight data and software-defined radio (SDR)-based Global Positioning System (GPS) spoofing, alongside simulated attacks. The results demonstrate exceptional performance, where the average anomaly detection accuracy exceeded 99% and the precision surpassed 98%. Notably, when benchmarked against traditional methods, the proposed system reduced navigation errors by a factor of approximately 2-3 under attack scenarios, which substantially enhanced the operational stability of the UAVs in challenging environments.https://www.mdpi.com/2072-4292/17/13/2176UAV photogrammetrymulti-source fusionsensor attacksinterference detection |
| spellingShingle | Chen Meng Haoyang Yang Cuicui Jiang Qinglei Hu Dongyu Li Improving UAV Remote Sensing Photogrammetry Accuracy Under Navigation Interference Using Anomaly Detection and Data Fusion Remote Sensing UAV photogrammetry multi-source fusion sensor attacks interference detection |
| title | Improving UAV Remote Sensing Photogrammetry Accuracy Under Navigation Interference Using Anomaly Detection and Data Fusion |
| title_full | Improving UAV Remote Sensing Photogrammetry Accuracy Under Navigation Interference Using Anomaly Detection and Data Fusion |
| title_fullStr | Improving UAV Remote Sensing Photogrammetry Accuracy Under Navigation Interference Using Anomaly Detection and Data Fusion |
| title_full_unstemmed | Improving UAV Remote Sensing Photogrammetry Accuracy Under Navigation Interference Using Anomaly Detection and Data Fusion |
| title_short | Improving UAV Remote Sensing Photogrammetry Accuracy Under Navigation Interference Using Anomaly Detection and Data Fusion |
| title_sort | improving uav remote sensing photogrammetry accuracy under navigation interference using anomaly detection and data fusion |
| topic | UAV photogrammetry multi-source fusion sensor attacks interference detection |
| url | https://www.mdpi.com/2072-4292/17/13/2176 |
| work_keys_str_mv | AT chenmeng improvinguavremotesensingphotogrammetryaccuracyundernavigationinterferenceusinganomalydetectionanddatafusion AT haoyangyang improvinguavremotesensingphotogrammetryaccuracyundernavigationinterferenceusinganomalydetectionanddatafusion AT cuicuijiang improvinguavremotesensingphotogrammetryaccuracyundernavigationinterferenceusinganomalydetectionanddatafusion AT qingleihu improvinguavremotesensingphotogrammetryaccuracyundernavigationinterferenceusinganomalydetectionanddatafusion AT dongyuli improvinguavremotesensingphotogrammetryaccuracyundernavigationinterferenceusinganomalydetectionanddatafusion |