An improved Kalman filter algorithm based on the "current" model
Zhao Xue-feng
Abstract
Zhao Xue-feng
Abstract
An improved Kalman algorithm based on the current model was presented to avoid the influence of the acceleration limits.The difference between the velocity forecast estimate and the corrected velocity estimate was utilized to perform adaptive acceleration variance adjustment.The simulation of Kalman algorithms with different acceleration limit parameters proved that the performance of Kalman filter was influenced by the acceleration limits.In addition,the improved Kalman algorithm was compared with standard Kalman filter.The results showed that the proposed method forecast more accurately than the standard Kalman filter.
OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
An improved Kalman algorithm based on the current model was presented to avoid the influence of the acceleration limits.The difference between the velocity forecast estimate and the corrected velocity estimate was utilized to perform adaptive acceleration variance adjustment.The simulation of Kalman algorithms with different acceleration limit parameters proved that the performance of Kalman filter was influenced by the acceleration limits.In addition,the improved Kalman algorithm was compared with standard Kalman filter.The results showed that the proposed method forecast more accurately than the standard Kalman filter.
Key concepts: Kalman filter, Fast Kalman filter, Acceleration, Alpha beta filter, Ensemble Kalman filter, Invariant extended Kalman filter, Extended Kalman filter, Algorithm