A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Re-entry Vehicle Tracking Using Adaptive Extended Kalman Filter from Discrete Noisy Measurements
https://orcid.org/http://orcid.org/0000-0002-9010-9717
Tracking of a ballistic vehicle (BV) flying in the Earth’s atmosphere by radar is a very complex issue to cope with and needs some (suboptimal) nonlinear filtering techniques for it. Further, if some characteristics or parameters of the vehicle are poorly known or unknown, it adds an identification problem to the tracking problem. In this paper, an adaptive extended Kalman filter (AEKF) is proposed for solution of tracking problem of a ballistic vehicle by processing radar measurements in the re-entry phase of its motion. The proposed AEKF algorithm is tested and examined for a typical BV tracking problem adopted from literature. In the current study, vehicle tracking problem has been formulated as (i) state estimation problem and (ii) joint parameter and state estimation problem. The effectiveness of the proposed AEKF algorithm is compared with most accepted extended Kalman filter in terms of estimation correctness and computational effort. The simulation results of the present work prove the potential of the AEKF in solving a non-linear re-entry phase target tracking problem.
Re-entry Vehicle Tracking Using Adaptive Extended Kalman Filter from Discrete Noisy Measurements
https://orcid.org/http://orcid.org/0000-0002-9010-9717
Tracking of a ballistic vehicle (BV) flying in the Earth’s atmosphere by radar is a very complex issue to cope with and needs some (suboptimal) nonlinear filtering techniques for it. Further, if some characteristics or parameters of the vehicle are poorly known or unknown, it adds an identification problem to the tracking problem. In this paper, an adaptive extended Kalman filter (AEKF) is proposed for solution of tracking problem of a ballistic vehicle by processing radar measurements in the re-entry phase of its motion. The proposed AEKF algorithm is tested and examined for a typical BV tracking problem adopted from literature. In the current study, vehicle tracking problem has been formulated as (i) state estimation problem and (ii) joint parameter and state estimation problem. The effectiveness of the proposed AEKF algorithm is compared with most accepted extended Kalman filter in terms of estimation correctness and computational effort. The simulation results of the present work prove the potential of the AEKF in solving a non-linear re-entry phase target tracking problem.
Re-entry Vehicle Tracking Using Adaptive Extended Kalman Filter from Discrete Noisy Measurements
https://orcid.org/http://orcid.org/0000-0002-9010-9717
Tracking of a ballistic vehicle (BV) flying in the Earth’s atmosphere by radar is a very complex issue to cope with and needs some (suboptimal) nonlinear filtering techniques for it. Further, if some characteristics or parameters of the vehicle are poorly known or unknown, it adds an identification problem to the tracking problem. In this paper, an adaptive extended Kalman filter (AEKF) is proposed for solution of tracking problem of a ballistic vehicle by processing radar measurements in the re-entry phase of its motion. The proposed AEKF algorithm is tested and examined for a typical BV tracking problem adopted from literature. In the current study, vehicle tracking problem has been formulated as (i) state estimation problem and (ii) joint parameter and state estimation problem. The effectiveness of the proposed AEKF algorithm is compared with most accepted extended Kalman filter in terms of estimation correctness and computational effort. The simulation results of the present work prove the potential of the AEKF in solving a non-linear re-entry phase target tracking problem.
Re-entry Vehicle Tracking Using Adaptive Extended Kalman Filter from Discrete Noisy Measurements
J. Inst. Eng. India Ser. C
Singh, Rudra Pratap (author) / Mukherjee, V. (author) / Prasad, Dharmbir (author)
Journal of The Institution of Engineers (India): Series C ; 106 ; 97-107
2025-02-01
11 pages
Article (Journal)
Electronic Resource
English
Re-entry Vehicle Tracking Using Adaptive Extended Kalman Filter from Discrete Noisy Measurements
| Springer Verlag | 2025
Multiple Vehicle 3D Tracking Using an Unscented Kalman Filter
| British Library Conference Proceedings | 2005
Moving Vehicle Tracking Based on Kalman Filter
| British Library Conference Proceedings | 2011
An adaptive extended Kalman filter for structural damage identification
| Online Contents | 2006