Enhanced Detection of Rotational Doppler Shift from Sunlight
Juedong Yang, Yuan Li, Wuhong Zhang, Lixiang Chen
TL;DR
This work addresses detecting rotational Doppler shifts (RDE) using sunlight, a partially coherent, broadband source, to enable passive remote sensing with light's orbital angular momentum (OAM). The authors implement a multi-wavelength spectral summation strategy and a laboratory setup that encodes OAM states via a spatial light modulator, followed by Fourier analysis of detected photons. They demonstrate that $\Delta f \propto \ell \Omega$ holds across multiple $\ell$ values and rotation rates, achieving measurable signals even under low photon flux when spectra from several wavelengths are combined. The approach significantly improves signal-to-noise ratio in natural-light conditions and paves the way for practical, passive RDE sensing across broader spectral regions, including potential infrared or ultraviolet implementations.
Abstract
The rotational Doppler effect, for which the frequency shift is proportional to the light's orbital angular momentum $\ell$ and the object's rotational speed ($Δf \propto\ell Ω$), has proven to be a powerful tool for detecting the speed of rotational objects. However, the current detection technique is mainly based on coherent laser sources. There is scarce mention of using partially coherent light sources, let alone sunlight. In this work, we collect sunlight and direct it into the laboratory, where it is modulated into a partially coherent probing source and then realize rotational Doppler shift detection. Our study reveals that in low-light conditions, where background noise is stronger than the signal, the superposition of rotational Doppler signals at different wavelengths can significantly enhance the signal strength and improve the signal-to-noise ratio, enabling accurate measurement of the rotational speed of objects. Our research provides experimental validation for the application of sunlight in rotational Doppler shift detection, demonstrating its potential value for passive remote sensing.
