A synchronous Extended Kalman Filter was used for integration, combining the heading and position into one calculating process. In the integration algorithm, we also use dynamic weighting of GNSS observations,
if desired. An optimal estimating system that is well suited for program implementation in a high speed digital computer is the estimator known as a Kalman filter. The Kalman filter is well known in the literature and may be defined as an optimal recursive filter that is based on space and ...
A synchronous Extended Kalman Filter was used for integration, combining the heading and position into one calculating process. In the integration algorithm, we also use dynamic weighting of GNSS observations, which is dependent on signal disturbances. The dynamic weighting of observations is an ...
A Kalman filter (KF), as the typical system identification filter, is widely used in the WIM area [20,21,22]. The filtered signal is codetermined using a system-state matrix and sensor-sampling value at each time. By using a covariance matrix for dynamic state update, the KF has been ...
The EnKF was developed as an extension of the original Kalman filter (KF) for non-linear dynamic models [42]. The EnKF, as the traditional KF, is a two-step approach. Both filters assume a state vector, 𝐱𝑡xt, that evolves in time following a Markov chain stochastic process. The ...
The EnKF was developed as an extension of the original Kalman filter (KF) for non-linear dynamic models [42]. The EnKF, as the traditional KF, is a two-step approach. Both filters assume a state vector, 𝐱𝑡xt, that evolves in time following a Markov chain stochastic process. The ...
This method can significantly improve computational accuracy and filter the noise caused by the system’s weighing process. However, some limitations remain, such as the real-time performance of the AF algorithm, which is constrained by its reliance on historical data for smoothing. In complex, ...
The demand for sensorless control of surface-mounted permanent magnet synchronous motor drives has grown rapidly. Among various sensorless control techniques developed, Matsui’s current model-based approach and the extended Kalman filter approach have gained much attention. However, the performance of the...
This method can significantly improve computational accuracy and filter the noise caused by the system's weighing process. However, some limitations remain, such as the real-time performance of the AF algorithm, which is constrained by its reliance on historical data for smoothing. In complex, ...