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Three-Dimensional Target Motion Analysis From Angle Measurements: A Multi-Agent-Based Method
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DOI：10.1109/LSP.2025.3533261.png)
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Abstract
En 中文
This letter is concerned with a three-dimensional target motion analysis issue using azimuth and elevation measurements. The nonlinear relationship between these measurements and target dynamics often poses challenges for conventional methods, especially in high-noise environments. To address this challenge, a novel multi-agent deep reinforcement learning (MADRL)-based estimator is proposed for target motion parameter estimation. Specifically, by modeling each component of the target motion parameter as an individual agent, the target motion parameter estimation process is framed as a cooperative Markov game. An MADRL framework is then introduced to solve this problem. Simulation results demonstrate that the proposed algorithm achieves higher estimation accuracy than existing estimators.
Keywords:
Noise measurement
Games
Three-dimensional displays
Motion measurement
Entropy
Azimuth
Motion analysis
Maximum likelihood estimation
Location awareness
Vectors
Three-dimensional
target motion analysis
angle measurements
multi-agent
Journal
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