Angle-only Maneuvering Target Tracking Using Primal-dual Gaussian Particle Filtering
To reduce the mapping basepoint offset and Gaussian truncation errors caused by spatiotemporal inconsistency in angle-only maneuvering target tracking systems, mapping representation and -ℓ1 ℓ2,1 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％.
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