|Abstract:||In this research, Advantage Actor-Critic(A2C) Reinforcement Learning algorithm is applied on high precision augmented navigation system with GPS/GLONASS dual frequency receiver and navigation grade INS. Despite the complementary navigation characteristics, GNSS and INS augmented system suffers from insufficient model error especially on altitude. It is caused by vertical divergence of INS and vertical error budget of GNSS such as ionospheric delay. The approach dealt in this paper is to train the system with neural network utilizing accurate post processed carrier phase differential GNSS position. The flight data for the research is obtained through 2.5-hour flight, including IMU measurements, raw GNSS range and ephemeris information, etc. In order to train the networks, a custom reinforcement learning environment was designed. The environment defined the interaction between agent and its surrounding, with state, action, and reward. Applied policy and value optimizing A2C algorithm for continuous action space is described. With proposed continuous action A2C, the network achieved higher score compared to the baseline. Increasing the number of trainable weights and biases resulted in enhancement of accuracy and stability.|
Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018)
September 24 - 28, 2018
Hyatt Regency Miami
|Pages:||3179 - 3185|
|Cite this article:||
Shin, Hyeoncheol, Lee, Jungshin, Sung, Chang-ky, "Implementation of Deep Reinforcement Learning on High Precision GNSS/INS Augmentation System," Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018), Miami, Florida, September 2018, pp. 3179-3185.
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