Enhanced Step Response Analysis via Multi-Modal Data Fusion and Recursive Filtering (SMDRF)

**Abstract:** This paper introduces a novel approach for analyzing step responses in dynamic systems leveraging multi-modal data fusion and recursive filtering techniques. Traditional step response analysis relies primarily on Time-Domain data. SMDRF expands this by integrating Frequency-Domain data and statistical characteristics, providing a richer, more robust assessment of system performance. By dynamically adjusting filter parameters based on observed data patterns, SMDRF achieves a significant improvement in accuracy and stability compared to conventional methods, leading to more reliable system identification and control design. The core innovation lies in the application of a dynamically weighted Kalman filter operating within a hyperdimensional space, allowing for the simultaneous and optimized assessment of multiple step response characteristics.