3D Coordinate-coupled Variable Structure Multiple Model Estimator for Maneuvering Target Tracking

ZHANG Hongwei; GAO Zhijian; ZHANG Yi

doi:10.11999/JEIT231290

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ZHANG Hongwei, GAO Zhijian, ZHANG Yi. 3D Coordinate-coupled Variable Structure Multiple Model Estimator for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231290

Citation:

ZHANG Hongwei, GAO Zhijian, ZHANG Yi. 3D Coordinate-coupled Variable Structure Multiple Model Estimator for Maneuvering Target Tracking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231290

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3D Coordinate-coupled Variable Structure Multiple Model Estimator for Maneuvering Target Tracking

doi: 10.11999/JEIT231290

1.
School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China
2.
ATR Key Laboratory, Shenzhen University, Shenzhen 518060, China

Funds: Sun Yat-sen University Youth Cultivation Project (20lgpy72), The Open Research Fund of CAS Key Laboratory of Space Precision Measurement Technology (SPMT2022001), The Key Project of DEGP (2020ZDZX1054)

Received Date: 2023-11-21
Rev Recd Date: 2024-04-17

Available Online: 2024-05-13

Abstract

Abstract

In the 3D maneuvering target tracking, unknown prior and coordinate coupling errors can cause model-mode mismatch and state estimation bias. In this paper, the state transition matrices are modified based on the target velocity-orthogonal condition, the spherical feasible domain is approximated by using the primal-dual regularization, and the adaptive turn rate model is combined in the frame of Unscented Kalman Filtering (UKF) to estimate the model-conditioned state, attaining the consistent output processing. 3D Variable Structure Multi-Model UKF (VSMMUKF) algorithm is derived. Simulation results show that, compared to the Multimode Importance UKF (MIUKF) algorithm, VSMMUKF can more accurately fit the maneuvering motion of 3D spatial point target with the comparable computational complexity; Compared to the Interactive Multi-model Maximum Minimum Particle Filtering (IMM-MPF) algorithm, the filtering accuracy of VSMMUKF for tracking a fixed-wing Unmanned Aerial Vehicle (UAV) has improved by 2.8%～59.9%, and the overall computation burden has reduced an order of magnitude.
- 3D Maneuvering target tracking,
- Coordinate-coupling,
- Adaptive turn rate,
- Variable structure multiple model,
- Nonlinear state estimation

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References(31)

References

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