Down Range Crosswind Sensing (DR-CWS)

To address the challenges in optically measuring the wind, nVision proposes exploring a modified system design in this Phase I effort, as well as investigate alternative processing methods, such as AI/ML models, to dynamically adapt to changing turbulence structures and incorporate parameters like range and daily atmospheric variations. By training on nVision’s extensive dataset and performing new data captures, these models could identify complex correlations between signal variation, ambient illumination, spot motion, and range-dependent fades, reducing the need for manual parameter tuning. In partnership with Case Western Reserve University (CWRU), nVision aims to develop a more robust wind measurement algorithm optimized for low-power hardware. These advancements aim to deliver a DRCWS that meets SOCOM’s need for reliable, downrange wind sensing, significantly enhancing operator lethality.