PENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments
Abstract Target tracking for fixed-wing unmanned aerial vehicles (UAVs) in complex urban environments faces challenges including potential target state loss and occlusion by multiple obstacles, typically large vertical structures like high-rise buildings. This necessitates tracking algorithms capabl...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-13095-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849387820050808832 |
|---|---|
| author | Shiji Hai Xitai Na Zhihui Feng Jinshuo Shi Qingbin Sun |
| author_facet | Shiji Hai Xitai Na Zhihui Feng Jinshuo Shi Qingbin Sun |
| author_sort | Shiji Hai |
| collection | DOAJ |
| description | Abstract Target tracking for fixed-wing unmanned aerial vehicles (UAVs) in complex urban environments faces challenges including potential target state loss and occlusion by multiple obstacles, typically large vertical structures like high-rise buildings. This necessitates tracking algorithms capable of both target state estimation and prediction. To address this, this paper proposes a Predictive-Estimative Nonlinear Control (PENC) framework. This framework optimizes the UAV-gimbal system’s control inputs in real-time to ensure the target remains within the camera’s field of view (FOV) despite obstacles, while simultaneously using historical target state measurements to accurately estimate its current state. When target state measurements is lost, PENC dynamically adjusts the measurement noise covariance matrix (R) and the process noise covariance matrix (Q) within the estimator via a unique weight-switching mechanism. This shifts reliance to the target’s dynamic model and historical data for state prediction. Simulations conducted in an environment simulating typical urban vertical obstacles demonstrate that the proposed method significantly outperforms both conventional Nonlinear Model Predictive Control (NMPC) and NMPC with Extended Kalman Filtering (NMPC-EKF) in UAV target tracking performance. This improvement is particularly evident during target measurements loss scenarios, effectively ensuring tracking continuity and robustness. Quantitative results show that PENC increases the Target Visibility Percentage (TVP) by up to 14 percentage points, reduces the Mean Recovery Time (MRT) to 0.03 seconds, and lowers the prediction Root Mean Square Error (RMSE) during state loss by $$74.5\%$$ . |
| format | Article |
| id | doaj-art-0dfa53e08e23433b8c8d7e4707396141 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0dfa53e08e23433b8c8d7e47073961412025-08-20T03:42:28ZengNature PortfolioScientific Reports2045-23222025-08-0115111910.1038/s41598-025-13095-zPENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environmentsShiji Hai0Xitai Na1Zhihui Feng2Jinshuo Shi3Qingbin Sun4School of Electronic and Information Engineering, Inner Mongolia UniversitySchool of Electronic and Information Engineering, Inner Mongolia UniversitySchool of Electronic and Information Engineering, Inner Mongolia UniversitySchool of Electronic and Information Engineering, Inner Mongolia UniversitySchool of Electronic and Information Engineering, Inner Mongolia UniversityAbstract Target tracking for fixed-wing unmanned aerial vehicles (UAVs) in complex urban environments faces challenges including potential target state loss and occlusion by multiple obstacles, typically large vertical structures like high-rise buildings. This necessitates tracking algorithms capable of both target state estimation and prediction. To address this, this paper proposes a Predictive-Estimative Nonlinear Control (PENC) framework. This framework optimizes the UAV-gimbal system’s control inputs in real-time to ensure the target remains within the camera’s field of view (FOV) despite obstacles, while simultaneously using historical target state measurements to accurately estimate its current state. When target state measurements is lost, PENC dynamically adjusts the measurement noise covariance matrix (R) and the process noise covariance matrix (Q) within the estimator via a unique weight-switching mechanism. This shifts reliance to the target’s dynamic model and historical data for state prediction. Simulations conducted in an environment simulating typical urban vertical obstacles demonstrate that the proposed method significantly outperforms both conventional Nonlinear Model Predictive Control (NMPC) and NMPC with Extended Kalman Filtering (NMPC-EKF) in UAV target tracking performance. This improvement is particularly evident during target measurements loss scenarios, effectively ensuring tracking continuity and robustness. Quantitative results show that PENC increases the Target Visibility Percentage (TVP) by up to 14 percentage points, reduces the Mean Recovery Time (MRT) to 0.03 seconds, and lowers the prediction Root Mean Square Error (RMSE) during state loss by $$74.5\%$$ .https://doi.org/10.1038/s41598-025-13095-zNonlinear Model Predictive ControlTracking TargetState EstimationNonlinear Moving Horizon EstimationFixed-Wing UAVs |
| spellingShingle | Shiji Hai Xitai Na Zhihui Feng Jinshuo Shi Qingbin Sun PENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments Scientific Reports Nonlinear Model Predictive Control Tracking Target State Estimation Nonlinear Moving Horizon Estimation Fixed-Wing UAVs |
| title | PENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments |
| title_full | PENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments |
| title_fullStr | PENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments |
| title_full_unstemmed | PENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments |
| title_short | PENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments |
| title_sort | penc a predictive estimative nonlinear control framework for robust target tracking of fixed wing uavs in complex urban environments |
| topic | Nonlinear Model Predictive Control Tracking Target State Estimation Nonlinear Moving Horizon Estimation Fixed-Wing UAVs |
| url | https://doi.org/10.1038/s41598-025-13095-z |
| work_keys_str_mv | AT shijihai pencapredictiveestimativenonlinearcontrolframeworkforrobusttargettrackingoffixedwinguavsincomplexurbanenvironments AT xitaina pencapredictiveestimativenonlinearcontrolframeworkforrobusttargettrackingoffixedwinguavsincomplexurbanenvironments AT zhihuifeng pencapredictiveestimativenonlinearcontrolframeworkforrobusttargettrackingoffixedwinguavsincomplexurbanenvironments AT jinshuoshi pencapredictiveestimativenonlinearcontrolframeworkforrobusttargettrackingoffixedwinguavsincomplexurbanenvironments AT qingbinsun pencapredictiveestimativenonlinearcontrolframeworkforrobusttargettrackingoffixedwinguavsincomplexurbanenvironments |