Low-Cost Real-Time Remote Sensing and Geolocation of Moving Targets via Monocular Bearing-Only Micro UAVs
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| Publicado en: | Remote Sensing vol. 17, no. 23 (2025), p. 3836-3857 |
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| Otros Autores: | , , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/rs17233836 |2 doi | |
| 035 | |a 3280962921 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Sun, Peng |u School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China; pengsun@std.uestc.edu.cn (P.S.); shijitong@std.uestc.edu.cn (S.T.); linbx@uestc.edu.cn (B.L.); maangat@uestc.edu.cn (M.S.) | |
| 245 | 1 | |a Low-Cost Real-Time Remote Sensing and Geolocation of Moving Targets via Monocular Bearing-Only Micro UAVs | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>A bearing-only localization framework is proposed for micro UAVs operating over uneven terrain, combining a pseudo-linear Kalman filter (PLKF) with a sliding-window nonlinear least squares optimization to achieve real-time 3D positioning and motion prediction. <list-item> An observability-enhanced flight trajectory planning method is designed based on the Fisher Information Matrix (FIM), which improves localization accuracy in flight experiments, with an average gain of up to 4.34 m. </list-item> What are the implications of the main findings? <list list-type="bullet"> <list-item> </list-item>The method addresses the limitations of monocular cameras with scale ambiguity and lack of depth measurement, providing a practical solution for low-cost UAVs in remote sensing and moving target localization. <list-item> The results suggest potential applications in emergency response, target monitoring, and traffic security, demonstrating the feasibility of high-accuracy localization on resource-constrained UAV platforms. </list-item> Low-cost and real-time remote sensing of moving targets is increasingly required in civilian applications. Micro unmanned aerial vehicles (UAVs) provide a promising platform for such missions because of their small size and flexible deployment, but they are constrained by payload capacity and energy budget. Consequently, they typically carry lightweight monocular cameras only. These cameras cannot directly measure distance and suffer from scale ambiguity, which makes accurate geolocation difficult. This paper tackles geolocation and short-term trajectory prediction of moving targets over uneven terrain using bearing-only measurements from a monocular camera. We present a two-stage estimation framework in which a pseudo-linear Kalman filter (PLKF) provides real-time state estimates, while a sliding-window nonlinear least-squares (NLS) back end refines them. Future target positions are obtained by extrapolating the estimated trajectory. To improve localization accuracy, we analyze the relationship between the UAV path and the Cramér–Rao lower bound (CRLB) using the Fisher Information Matrix (FIM) and derive an observability-enhanced trajectory planning method. Real-flight experiments validate the framework, showing that accurate geolocation can be achieved in real time using only low-cost monocular bearing measurements. | |
| 653 | |a Lower bounds | ||
| 653 | |a Depth measurement | ||
| 653 | |a Accuracy | ||
| 653 | |a Cramer-Rao bounds | ||
| 653 | |a Estimates | ||
| 653 | |a Depth perception | ||
| 653 | |a Optimization | ||
| 653 | |a Energy budget | ||
| 653 | |a Cameras | ||
| 653 | |a Remote sensing | ||
| 653 | |a Emergency response | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Flight | ||
| 653 | |a Localization | ||
| 653 | |a Emergency preparedness | ||
| 653 | |a Kalman filters | ||
| 653 | |a Low cost | ||
| 653 | |a Planning | ||
| 653 | |a Moving targets | ||
| 653 | |a Algorithms | ||
| 653 | |a Fisher information | ||
| 653 | |a Real time | ||
| 653 | |a Constraints | ||
| 653 | |a Ambiguity | ||
| 653 | |a Trajectory planning | ||
| 653 | |a Least squares | ||
| 653 | |a Terrain | ||
| 653 | |a Sliding | ||
| 700 | 1 | |a Tong Shiji |u School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China; pengsun@std.uestc.edu.cn (P.S.); shijitong@std.uestc.edu.cn (S.T.); linbx@uestc.edu.cn (B.L.); maangat@uestc.edu.cn (M.S.) | |
| 700 | 1 | |a Qin Kaiyu |u School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China; pengsun@std.uestc.edu.cn (P.S.); shijitong@std.uestc.edu.cn (S.T.); linbx@uestc.edu.cn (B.L.); maangat@uestc.edu.cn (M.S.) | |
| 700 | 1 | |a Luo Zhenbing |u College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China; luozhenbing@163.com | |
| 700 | 1 | |a Lin Boxian |u School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China; pengsun@std.uestc.edu.cn (P.S.); shijitong@std.uestc.edu.cn (S.T.); linbx@uestc.edu.cn (B.L.); maangat@uestc.edu.cn (M.S.) | |
| 700 | 1 | |a Shi Mengji |u School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China; pengsun@std.uestc.edu.cn (P.S.); shijitong@std.uestc.edu.cn (S.T.); linbx@uestc.edu.cn (B.L.); maangat@uestc.edu.cn (M.S.) | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 23 (2025), p. 3836-3857 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3280962921/abstract/embedded/BP4M5IEWWR03UZF2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3280962921/fulltextwithgraphics/embedded/BP4M5IEWWR03UZF2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3280962921/fulltextPDF/embedded/BP4M5IEWWR03UZF2?source=fedsrch |