A UAV multi-agent countermeasures behavior planning for improving positioning

Jingyan Huang; Wei Chen; Yifan Tan; Peiqin Li

doi:10.1117/12.2645489

23 August 2022 A UAV multi-agent countermeasures behavior planning for improving positioning

Jingyan Huang, Wei Chen, Yifan Tan, Peiqin Li

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 123050H (2022) https://doi.org/10.1117/12.2645489
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

The positioning system based on the time difference of arrival (TDOA) has a systematic error in a specific direction. This paper uses the Multi-agent Deep Deterministic Policy Gradient (MADDPG) method for environmental modeling to improve the error distribution of the positioning system. The element configuration and execution steps of reinforcement learning modeling are analyzed in detail. And then the travel path is planned for the purpose of improving the positioning accuracy. At the same time, the model of the positioned unit under the confrontation condition is established to better get rid of the positioning. In the simulation environment, the camp of unmanned aerial vehicle (UAV) constantly learns and adjusts the strategy to detect the strategy of enemy using the effective area critical line to avoid positioning. The training process is summarized, and suggestions are put forward for the confrontation decision of both camps.

Citation Download Citation

Jingyan Huang, Wei Chen, Yifan Tan, and Peiqin Li "A UAV multi-agent countermeasures behavior planning for improving positioning", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 123050H (23 August 2022); https://doi.org/10.1117/12.2645489

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