Khái niệm cốt lõi
FlexKalmanNet is a novel modular framework that integrates a deep fully connected neural network with Kalman filter-based motion estimation algorithms, enabling the learning of any Kalman filter parameter directly from measurement data and the flexibility to utilize various Kalman filter variants.
Tóm tắt
The paper introduces FlexKalmanNet, a novel modular framework that combines a deep fully connected neural network (DFCNN) with a Kalman filter (KF) variant, specifically the Extended Kalman Filter (EKF), for spacecraft motion estimation.
The key highlights of the framework are:
FlexKalmanNet can learn any KF parameter, such as the process noise covariance (Q) and measurement noise covariance (R), directly from measurement data, without requiring extensive manual tuning.
The framework allows the use of different KF variants, providing flexibility to adapt to various scenarios and computational requirements.
A critical innovation is the outsourcing of the recursive computations from the neural network to the KF, enabling the use of a purely feedforward neural network architecture. This architecture is capable of capturing complex, nonlinear features more effectively than recurrent network modules.
The authors evaluate FlexKalmanNet in a spacecraft scenario, using data from the NASA Astrobee simulation environment. The results demonstrate FlexKalmanNet's rapid training convergence, high accuracy, and superior performance compared to manually tuned EKFs. The learned EKF parameters exhibit stability, robustness to noise, and adaptability to different angular velocity conditions.
The paper concludes by discussing the limitations, ongoing tasks, and future challenges, such as exploring the use of more advanced dynamics models and investigating learning beyond just the diagonal covariance matrix entries.
Thống kê
The standard deviations of the learned noise covariance parameters for Dataset 1 (DS1) are:
σRqw = 0.042746
σRqx = 0.036301
σRqy = 0.048697
σRqz = 0.029550
σRrx = 0.078810
σRry = 0.058190
σRrz = 0.082663
σQqw = 0.000161
σQqx = 0.000090
σQqy = 0.000071
σQqz = 0.000026
σQrx = 0.000150
σQry = 0.000010
σQrz = 0.000132
σQωx = 0.000079
σQωy = 0.000145
σQωz = 0.000037
σQvx = 0.000023
σQvy = 0.000028
σQvz = 0.000025
Trích dẫn
"FlexKalmanNet's core innovation is its ability to learn any Kalman filter parameter directly from measurement data, coupled with the flexibility to utilize various Kalman filter variants."
"A critical innovation in this framework is the outsourcing of the recursive computations from the neural network to the Kalman filter. This design choice enables the use of a purely feedforward neural network architecture, adept at mapping complex and nonlinear features without relying on recurrent network modules."