RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles
| العنوان: | RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles |
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| المؤلفون: | Yimin Xiao, Yilin Gong, Bokun Ning, Fang Zhao, Bao Linfeng, Jinguang Jiang, Haiyong Luo, Xile Gao |
| المصدر: | Remote Sensing, Vol 12, Iss 1704, p 1704 (2020) Remote Sensing; Volume 12; Issue 11; Pages: 1704 |
| بيانات النشر: | MDPI AG, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | reinforcement learning, 050210 logistics & transportation, 0209 industrial biotechnology, process noise covariance estimation, Covariance matrix, Computer science, Science, 05 social sciences, Navigation system, integrated navigation, 02 engineering and technology, Kalman filter, Filter (signal processing), Odometer, Estimation of covariance matrices, 020901 industrial engineering & automation, deep deterministic policy gradient, GNSS applications, 0502 economics and business, General Earth and Planetary Sciences, Algorithm, Inertial navigation system |
| الوصف: | Kalman filter is a commonly used method in the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system, in which the process noise covariance matrix has a significant influence on the positioning accuracy and sometimes even causes the filter to diverge when using the process noise covariance matrix with large errors. Though many studies have been done on process noise covariance estimation, the ability of the existing methods to adapt to dynamic and complex environments is still weak. To obtain accurate and robust localization results under various complex and dynamic environments, we propose an adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach. By taking the integrated navigation system as the environment, and the opposite of the current positioning error as the reward, the adaptive Kalman filter navigation algorithm uses the deep deterministic policy gradient to obtain the most optimal process noise covariance matrix estimation from the continuous action space. Extensive experimental results show that our proposed algorithm can accurately estimate the process noise covariance matrix, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes. The RL-AKF achieves an average positioning error of 0.6517 m within 10 s GNSS outage for GNSS/INS integrated navigation system and 14.9426 m and 15.3380 m within 300 s GNSS outage for the GNSS/INS/Odometer (ODO) and the GNSS/INS/Non-Holonomic Constraint (NHC) integrated navigation systems, respectively. |
| وصف الملف: | application/pdf |
| تدمد: | 2072-4292 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::505c9612fc1fe8a6a3579cea76b894d4Test https://doi.org/10.3390/rs12111704Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....505c9612fc1fe8a6a3579cea76b894d4 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20724292 |
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