Angle-only Maneuvering Target Tracking Using Primal-dual Gaussian Particle Filtering

ZHANG Hongwei

doi:10.11999/JEIT230413

Volume 46 Issue 4

Apr. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(4): 1408-1417

ZHANG Hongwei. Angle-only Maneuvering Target Tracking Using Primal-dual Gaussian Particle Filtering[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1408-1417. doi: 10.11999/JEIT230413

Citation:

ZHANG Hongwei. Angle-only Maneuvering Target Tracking Using Primal-dual Gaussian Particle Filtering[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1408-1417. doi: 10.11999/JEIT230413

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Angle-only Maneuvering Target Tracking Using Primal-dual Gaussian Particle Filtering

doi: 10.11999/JEIT230413

ZHANG Hongwei^,

School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China

Funds: Guang Dong Basic and Applied Fundamental Research Fund Project (2019A1515111099), Sun Yat-sen University Youth Cultivation Project (20lgpy72), The Open Research Fund of CAS Key Laboratory of Space Precision Measurement Technology (SPMT2021002, SPMT2022001)

Received Date: 2023-05-15
Rev Recd Date: 2023-06-30

Available Online: 2023-07-06

Publish Date: 2024-04-24

Abstract

Abstract

To reduce the mapping basepoint offset and Gaussian truncation errors caused by spatiotemporal inconsistency in angle-only maneuvering target tracking systems, mapping representation and $ {\ell _1} $-$ {\ell _{2,1}} $ sparse regularization to represent spatiotemporal causal consistency constraints are used, the fuzzy comprehensive closeness is introduced to establish the suboptimal proposal distribution, the particle set in a causal invariant structure to approximate the Gaussian integration for target posterior is propagated, and the Primal-Dual Gaussian Particle Filtering (PDGPF) algorithm is derived. Simulation results show that, compared to the intersection measurement method with least squares, the accuracy for the PDGPF to locate a rotor Unmanned Aerial Vehicle (UAV) has improved by 18.4%～69.6%. Compared to the Soft Constrained Auxiliary Particle Filtering (SCAPF) algorithm, the PDGPF algorithm can adaptively correct the particle weights under the spatiotemporal mapping consistent constraints, obtaining more accurate and stable state estimation for tracking a maneuvering point target, reducing the overall computational burden by 12.9%.
- Maneuvering target tracking,
- Angle-only,
- Spatiotemporal inconsistency,
- Primal-dual,
- Causal invariant

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References(32)

References

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