Decoupling Spatio-Temporal Dynamics: Microvibration Imaging Using Coherent Detection Ghost Imaging Lidar

Abstract

Imaging the full-field microvibration of extended targets remains a formidable challenge for conventional remote sensing. Traditional array-based sensors are often severely constrained by data throughput and sensitivity limits when scaling to high spatial resolutions, while point-scanning interferometric systems lack the instantaneous full-field capability required to capture transient, spatially coupled vibration modes. To overcome these limitations, we propose a Coherent Detection-Ghost Imaging (CD-GI) framework that synergizes the spatial multiplexing capability of single-pixel imaging with the high-dimensional sensitivity of coherent detection. We establish a comprehensive mathematical model that describes the coupling mechanism of the target's spatial distribution and temporal micro-dynamics within a 1D bucket detector signal. To resolve the resulting inverse problem, we develop a frequency-channel self-calibration scheme. This approach effectively decouples the micro-Doppler signatures from spatial speckle patterns without requiring prior knowledge of the vibration frequency. Experimental results demonstrate that our system successfully reconstructs the spatially resolved microvibration patterns of adjacent targets with a frequency difference as small as 1 Hz, achieving sub-wavelength vibration sensitivity. This work bridges the gap between computational imaging and coherent metrology, offering a robust solution for non-invasive, high-precision structural health monitoring.

Figures (9)

• Figure 1: Schematic diagram of the proposed CD-GI lidar system. The architecture is based on a Mach-Zehnder interferometer where the transmitting path is frequency-shifted by an AOM and spatially modulated by a DMD. The backscattered echo interferes with the $LO$ at a balanced detector, converting the high-dimensional spatio-temporal target information into a one-dimensional heterodyne signal.
• Figure 2: A magnified view of a specific probing area on the target surface during detection. The initial phase distribution and amplitude profile across the target surface collectively constitute the vibration mode morphology of the target.
• Figure 3: A magnified view of a specific probing area on the target surface during detection. The initial phase distribution and amplitude profile across the target surface collectively constitute the vibration mode morphology of the target. On this time-frequency spectrum, the overlapping energy trajectories contributed by scattering points with varying amplitudes and initial phases across the target surface are displayed simultaneously. Similarly, this composite spectrum can be decomposed into multiple distinct, individual time-frequency energy trajectories based on their specific amplitude and initial phase characteristics.
• Figure 4: Simulation result related to the performance of the frequency selectivity. (a) displays the coherent gain curves of two point targets when $f_{v1}$ is set to 1200Hz and $f_{v2}$ is varied. The green region indicates the 3dB bandwidth, which corresponds to the high match zone. The red region represents the low match zone. (b) illustrates how the coherent gain curve evolves under different vibration amplitudes. It is clearly visible that as the modulation depth of the Bessel function increases, the coherence degrades more rapidly with oscillations, as expected from the theoretical model.
• Figure 5: A satellite view of the experimental site. The top-left corner shows a detailed image of the target area.