Online evaluation of the process noise covariance matrix for event‐based state estimators

2019 ◽  
Vol 120 (2) ◽  
pp. 195-208
Author(s):  
Miguel Martínez‐Rey ◽  
Carlos Santos ◽  
Rubén Nieto ◽  
Cristina Losada ◽  
Felipe Espinosa
Keyword(s):  
Covariance Matrix ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
Online Evaluation ◽  
State Estimators ◽  
Noise Covariance ◽  
Event Based

A New Process Noise Covariance Matrix Tuning Algorithm for Kalman Based State Estimators

2009 ◽  
Vol 42 (11) ◽  
pp. 572-577
Author(s):  
Nina P.G. Salau ◽  
Jorge O. Trierweiler ◽  
Argimiro R. Secchi ◽  
Wolfgang Marquardt
Keyword(s):  
Covariance Matrix ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
State Estimators ◽  
New Process ◽  
Noise Covariance

scholarly journals RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles

2020 ◽  
Vol 12 (11) ◽  
pp. 1704
Author(s):  
Xile Gao ◽  
Haiyong Luo ◽  
Bokun Ning ◽  
Fang Zhao ◽  
Linfeng Bao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Navigation System ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
Integrated Navigation System ◽  
Navigation Algorithm ◽  
Noise Covariance

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.

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