Combination of FIR and Kalman Filters to Increase Measurement Accuracy in Dynamic Weighing

Abstract

This study investigates the application of a combination of Finite Impulse Response (FIR) and Kalman filters to improve measurement accuracy in dynamic weighing processes. Dynamic environments, characterized by moving objects and varying conditions, pose challenges such as noise and signal losses, which can adversely affect measurement results. To address these issues, FIR filtering is employed to preprocess the data, effectively removing low-frequency noise. The cleaned data is then processed using a Kalman filter, minimizing errors at each step. The Kalman filter has proven effective in improving measurement accuracy by making predictions on noisy data. Consequently, the combined use of FIR and Kalman filters enables the achievement of reliable and accurate measurement results in dynamic weighing processes. This approach offers practical solutions to dynamic weighing problems in various application areas.

Keywords

• FIR filter
• Kalman filter
• Estimation algorithms

References

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