Akademik Veri Yönetim Sistemi
Advances in Engineering and Intelligence Systems, cilt.4, sa.1, ss.1-26, 2025 (Hakemli Dergi)
The Kalman filter is a widely employed algorithm for state estimation and sensor fusion in various fields. However, its performance can degrade in the presence of modeling errors and uncertainties in the system dynamics. To enhance the robustness and accuracy of the Kalman filter, the concept of a Fading-Memory Kalman Filter (FMKF) has been introduced. The FMKF incorporates a fadingmemory mechanism that effectively mitigates the impact of modeling errors and reduces permanent estimation errors. By assigning time-varying weights to past measurements and predictions, the FMKF adaptively adjusts its influence on the current state estimation. This mechanism allows the FMKF to better accommodate dynamic system behavior and parameter changes. In this paper, the effectiveness of the FMKF is evaluated by comparing it with the standard Kalman filter. The performance of both filters is assessed in scenarios where modeling errors are present, and parameter variations occur. The results demonstrate that the FMKF outperforms the standard Kalman filter by providing more accurate and robust state estimates, even in the presence of modeling errors. The FMKF's ability to adaptively update the weights based on the relevance of past information allows it to effectively handle dynamic system behavior and changing parameters.