Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target
The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/17/4239 |
| _version_ | 1797493369637699584 |
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| author | Fansen Zhou Yidi Wang Wei Zheng Zhao Li Xin Wen |
| author_facet | Fansen Zhou Yidi Wang Wei Zheng Zhao Li Xin Wen |
| author_sort | Fansen Zhou |
| collection | DOAJ |
| description | The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target. |
| first_indexed | 2024-03-10T01:19:03Z |
| format | Article |
| id | doaj.art-78b194d2f81a4989a15e69e7c7605d2c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:19:03Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-78b194d2f81a4989a15e69e7c7605d2c2023-11-23T14:03:17ZengMDPI AGRemote Sensing2072-42922022-08-011417423910.3390/rs14174239Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering TargetFansen Zhou0Yidi Wang1Wei Zheng2Zhao Li3Xin Wen4College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaThe newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target.https://www.mdpi.com/2072-4292/14/17/4239non-cooperative target trackingdistributed multiple-modeladaptive filterWasserstein distance |
| spellingShingle | Fansen Zhou Yidi Wang Wei Zheng Zhao Li Xin Wen Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target Remote Sensing non-cooperative target tracking distributed multiple-model adaptive filter Wasserstein distance |
| title | Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target |
| title_full | Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target |
| title_fullStr | Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target |
| title_full_unstemmed | Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target |
| title_short | Fast Distributed Multiple-Model Nonlinearity Estimation for Tracking the Non-Cooperative Highly Maneuvering Target |
| title_sort | fast distributed multiple model nonlinearity estimation for tracking the non cooperative highly maneuvering target |
| topic | non-cooperative target tracking distributed multiple-model adaptive filter Wasserstein distance |
| url | https://www.mdpi.com/2072-4292/14/17/4239 |
| work_keys_str_mv | AT fansenzhou fastdistributedmultiplemodelnonlinearityestimationfortrackingthenoncooperativehighlymaneuveringtarget AT yidiwang fastdistributedmultiplemodelnonlinearityestimationfortrackingthenoncooperativehighlymaneuveringtarget AT weizheng fastdistributedmultiplemodelnonlinearityestimationfortrackingthenoncooperativehighlymaneuveringtarget AT zhaoli fastdistributedmultiplemodelnonlinearityestimationfortrackingthenoncooperativehighlymaneuveringtarget AT xinwen fastdistributedmultiplemodelnonlinearityestimationfortrackingthenoncooperativehighlymaneuveringtarget |