Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware
Recently, Unmanned Aerial Vehicles (UAVs) have made significant impacts on our daily lives with the advancement of technologies and their applications. Tracking UAVs have become more important because they not only provide location-based services, but are also faced with serious security threats and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/24/3067 |
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| author | Mohammed Abdulhakim Al-Absi Rui Fu Ki-Hwan Kim Young-Sil Lee Ahmed Abdulhakim Al-Absi Hoon-Jae Lee |
| author_facet | Mohammed Abdulhakim Al-Absi Rui Fu Ki-Hwan Kim Young-Sil Lee Ahmed Abdulhakim Al-Absi Hoon-Jae Lee |
| author_sort | Mohammed Abdulhakim Al-Absi |
| collection | DOAJ |
| description | Recently, Unmanned Aerial Vehicles (UAVs) have made significant impacts on our daily lives with the advancement of technologies and their applications. Tracking UAVs have become more important because they not only provide location-based services, but are also faced with serious security threats and vulnerabilities. UAVs are smaller in nature, move with high speed, and operate in a low-altitude environment, which makes it conceivable to track UAVs using fixed or mobile radars. Kalman Filter (KF)-based methodologies are widely used for extracting valuable trajectory information from samples composed of noisy information. As UAVs’ trajectories resemble uncertain behavior, the traditional KF-based methodologies have poor tracking accuracy. Recently, the Diffusion-Map-based KF (DMK) was introduced for modeling uncertainties in the environment without prior knowledge. However, the model has poor accuracy when operating in environments with higher noise. In order to achieve better tracking performance, this paper presents the Uncertainty and Error-Aware KF (UEAKF) for tracking UAVs. The UEAKF-based tracking method provides a good tradeoff among preceding estimate confidence and forthcoming measurement under dynamic environments; the resulting filter is robust and nonlinear in nature. The experimental results showed that the UEAKF-based UAV tracking model achieves much better Root Mean Square Error (RMSE) performance compared to the existing particle filter-based and DMK-based UAV tracking models. |
| first_indexed | 2024-03-10T04:15:33Z |
| format | Article |
| id | doaj.art-f28052c021074b8cba48e08d3c3943a9 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T04:15:33Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-f28052c021074b8cba48e08d3c3943a92023-11-23T08:01:38ZengMDPI AGElectronics2079-92922021-12-011024306710.3390/electronics10243067Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error AwareMohammed Abdulhakim Al-Absi0Rui Fu1Ki-Hwan Kim2Young-Sil Lee3Ahmed Abdulhakim Al-Absi4Hoon-Jae Lee5Department of Ubiquitous IT, Graduate School, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, KoreaBlockchain Laboratory of Agriculture and Vegetables, Weifang University of Science and Technology, Weifang 262700, ChinaInternational College, Dongseo University, Busan 47011, KoreaInternational College, Dongseo University, Busan 47011, KoreaDepartment of Smart Computing, Kyungdong University, 46 4-gil, Bongpo, Gosung 24764, KoreaDivision of Information and Communication Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, KoreaRecently, Unmanned Aerial Vehicles (UAVs) have made significant impacts on our daily lives with the advancement of technologies and their applications. Tracking UAVs have become more important because they not only provide location-based services, but are also faced with serious security threats and vulnerabilities. UAVs are smaller in nature, move with high speed, and operate in a low-altitude environment, which makes it conceivable to track UAVs using fixed or mobile radars. Kalman Filter (KF)-based methodologies are widely used for extracting valuable trajectory information from samples composed of noisy information. As UAVs’ trajectories resemble uncertain behavior, the traditional KF-based methodologies have poor tracking accuracy. Recently, the Diffusion-Map-based KF (DMK) was introduced for modeling uncertainties in the environment without prior knowledge. However, the model has poor accuracy when operating in environments with higher noise. In order to achieve better tracking performance, this paper presents the Uncertainty and Error-Aware KF (UEAKF) for tracking UAVs. The UEAKF-based tracking method provides a good tradeoff among preceding estimate confidence and forthcoming measurement under dynamic environments; the resulting filter is robust and nonlinear in nature. The experimental results showed that the UEAKF-based UAV tracking model achieves much better Root Mean Square Error (RMSE) performance compared to the existing particle filter-based and DMK-based UAV tracking models.https://www.mdpi.com/2079-9292/10/24/3067Kalman filternon-parametric filteringsecuritystochastic environmenttrackingunmanned aerial vehicle |
| spellingShingle | Mohammed Abdulhakim Al-Absi Rui Fu Ki-Hwan Kim Young-Sil Lee Ahmed Abdulhakim Al-Absi Hoon-Jae Lee Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware Electronics Kalman filter non-parametric filtering security stochastic environment tracking unmanned aerial vehicle |
| title | Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware |
| title_full | Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware |
| title_fullStr | Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware |
| title_full_unstemmed | Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware |
| title_short | Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware |
| title_sort | tracking unmanned aerial vehicles based on the kalman filter considering uncertainty and error aware |
| topic | Kalman filter non-parametric filtering security stochastic environment tracking unmanned aerial vehicle |
| url | https://www.mdpi.com/2079-9292/10/24/3067 |
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