Abstract:
This letter is concerned with a three-dimensional target motion analysis issue using azimuth and elevation measurements. The nonlinear relationship between these measurem...Show MoreMetadata
Abstract:
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.
Published in: IEEE Signal Processing Letters ( Volume: 32)
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- IEEE Keywords
- Index Terms
- Angle Measurements ,
- Three-dimensional Analysis ,
- Three-dimensional Motion ,
- Three-dimensional Motion Analysis ,
- Target Motion Analysis ,
- Parameter Estimates ,
- Individual Agency ,
- Deep Reinforcement Learning ,
- Cooperative Game ,
- Maximum Likelihood Estimation ,
- Cost Function ,
- State Space ,
- Fixed Point ,
- Network Parameters ,
- Measurement Noise ,
- Estimation Problem ,
- Constant Velocity ,
- Azimuth Angle ,
- Measurement Period ,
- Threshold Effect ,
- Presence Of Measurement Noise ,
- Elevation Angle ,
- Reward Function ,
- Action-value Function ,
- Maximum Entropy ,
- Policy Agencies ,
- Paired Measurements ,
- Iterative Evaluation
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Angle Measurements ,
- Three-dimensional Analysis ,
- Three-dimensional Motion ,
- Three-dimensional Motion Analysis ,
- Target Motion Analysis ,
- Parameter Estimates ,
- Individual Agency ,
- Deep Reinforcement Learning ,
- Cooperative Game ,
- Maximum Likelihood Estimation ,
- Cost Function ,
- State Space ,
- Fixed Point ,
- Network Parameters ,
- Measurement Noise ,
- Estimation Problem ,
- Constant Velocity ,
- Azimuth Angle ,
- Measurement Period ,
- Threshold Effect ,
- Presence Of Measurement Noise ,
- Elevation Angle ,
- Reward Function ,
- Action-value Function ,
- Maximum Entropy ,
- Policy Agencies ,
- Paired Measurements ,
- Iterative Evaluation
- Author Keywords