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    Ghost imaging LiDAR with sparsity constraints and push-broom scanning simplifies systems and reduces sampling needs for continuous target scene imaging. This novel approach leverages relative motion for efficient data acquisition, validated by simulations and experiments.

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    Area of Science:

    • Optics and Photonics
    • Remote Sensing Technology
    • Computational Imaging

    Background:

    • Conventional ghost imaging LiDAR requires multiple speckle patterns for target imaging.
    • Existing methods can be complex and require significant sampling numbers.
    • Continuous imaging of stationary scenes presents a challenge for LiDAR systems.

    Purpose of the Study:

    • To propose a novel ghost imaging LiDAR method utilizing sparsity constraints and push-broom scanning.
    • To simplify the ghost imaging LiDAR system architecture.
    • To reduce the number of samples required for imaging stationary target scenes.

    Main Methods:

    • Implementing ghost imaging LiDAR with sparsity constraints.
    • Utilizing a push-broom scanning technique.
    • Leveraging the relative motion between the platform and the target scene for continuous scanning.

    Main Results:

    • The proposed method enables continuous imaging of stationary target scenes along the scanning direction.
    • The system complexity is reduced compared to conventional ghost imaging LiDAR.
    • The required sampling number is significantly decreased.
    • Numerical simulations and experimental results confirm the method's efficiency.

    Conclusions:

    • Ghost imaging LiDAR via sparsity constraints using push-broom scanning offers an efficient and simplified approach for target scene imaging.
    • This technique effectively utilizes relative motion for continuous data acquisition.
    • The method demonstrates practical viability and efficiency through simulations and experiments.