Performance Comparison of Kalman Filter and Extended Kalman Filter for Sensor Fusion
Keywords:
Adaptive filtering, Anomaly detection, Embedded systems, Extended Kalman filter, Kalman filter, MHD sensorsAbstract
Embedded systems depend on sensor fusion because it enhances their ability to estimate states accurately and reliably through multiple sensor measurements, which contain errors. The research paper establishes performance benchmarks for the Kalman Filter (KF) and the Extended Kalman Filter (EKF) as they operate within embedded sensor fusion systems. The KF establishes an optimal method for recursive estimation of linear systems that experience Gaussian noise, while the EKF enables nonlinear system estimation through its first-order linearization method, which uses Taylor series expansion. The unified framework, which supports both filters on embedded platforms, addresses three key constraints, namely limited computational resources, limited memory, and real-time processing requirements. The research study uses multi-sensor data simulation to assess three factors, which include estimation accuracy, convergence rate and computational complexity. The results demonstrate that KF operates efficiently with low computational requirements in linear systems, while EKF delivers better accuracy for nonlinear situations but needs more complex resources to function. The study results demonstrate that there exists an essential relationship between accuracy and processing efficiency. The KF method works best for systems that have restricted computational power and show behavior that closely resembles linearity, while the EKF method becomes necessary for handling nonlinear system behavior.
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