Enhanced State Estimation Using Improved Adaptive Filter

This paper presents an improved adaptive filtering method for state estimation that delivers outstanding performance. The filter is implemented in MATLAB and can be executed directly within the MATLAB environment to observe its superior filtering effects. The implementation utilizes the Sage-Husa adaptive algorithm, which features: - Real-time estimation of noise statistics (both process and measurement noise) - Adaptive correction of covariance matrices - Enhanced robustness to varying system conditions Compared to conventional filters, this improved version demonstrates substantial performance gains in: - Estimation accuracy under uncertain noise conditions - Convergence speed and stability - Practical application reliability The MATLAB implementation includes key functions for: - Initial parameter configuration and system modeling - Recursive calculation of adaptive factors - Real-time state prediction and correction cycles This enhancement provides significant performance benefits, making the filter more reliable and effective for practical engineering applications, particularly in scenarios with unknown or time-varying noise characteristics.