An Adaptive Multi-Model Higher-Order Hybrid Filtering Method for Maneuvering Target Trajectory Estimation

Abstract

To improve trajectory estimation for highly maneuvering aerial targets under time-varying noise, an adaptive multiple-model filtering framework is developed based on Kalman filtering. The framework integrates online model-probability updating, higher-order error-propagation correction, and adaptive noise adjustment to accommodate motion-mode transitions, nonlinear estimation errors, and measurement uncertainty. Simulation results show that, at a relative velocity of 800 m/s, the proposed method reduces the position root-mean-square error (RMSE) by 57.73% compared with the raw measurements and outperforms the individual motion-model filters. A field-programmable gate array (FPGA)-based laboratory validation platform is further developed, and the experimental results are consistent with the simulation results. The measured single-frame processing latency is 152 μs, demonstrating the effectiveness and real-time feasibility of the proposed framework for maneuvering-target trajectory estimation.