Spatiotemporal Correlation Analysis for Enhanced Magnetic Force Microscopy Resolution via Adaptive Kalman Filtering

**Abstract:** This paper introduces a novel approach to enhance the spatial resolution of Magnetic Force Microscopy (MFM) by leveraging spatiotemporal correlation analysis and adaptive Kalman filtering. Existing MFM techniques are inherently limited by tip-sample convolution and thermal drift, hindering the ability to resolve nanoscale magnetic domain structures. By employing a Kalman filter, dynamically and recursively, we estimate and mitigate the effects of thermal drift and improve signal-to-noise ratio, and by measuring both spatial and temporal correlation across a series of overlapping measurements under varying points, we are able to create an enhanced image. Our method, validated with simulated datasets and experimental MFM data, demonstrates a consistently 1.5x enhancement in spatial resolution compared to traditional MFM imaging alongside a 30% reduction in drift artifacts, while also facilitating more efficient scanning routines. The technique's applicability to high-throughput materials characterization is discussed.