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Image-free few-target tracking via single-pixel sensing.

Xuan Zhang, Jiahao Xiong, Ai Fu

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    |February 20, 2026
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    Summary
    This summary is machine-generated.

    This study introduces an image-free, single-pixel sensing framework for tracking multiple moving targets. The system effectively suppresses background noise and achieves accurate multi-target localization using an all-optical encoder and nonlinear model.

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    Area of Science:

    • Optoelectronics
    • Single-pixel sensing
    • Image-free tracking

    Background:

    • Single-pixel sensing traditionally focuses on single-target analysis.
    • Existing methods lack efficient background suppression for complex scenes.
    • Simultaneous localization of multiple targets remains a challenge.

    Purpose of the Study:

    • To develop an optoelectronic framework for image-free, multi-target tracking.
    • To enable background suppression in single-pixel sensing applications.
    • To advance single-pixel sensing beyond single-target scenarios.

    Main Methods:

    • Integration of a Fourier filter for background suppression.
    • Utilizing a nonlinear power-law model to encode spatial positions.
    • Employing least-squares inversion with cross-target optimization for decoding.

    Main Results:

    • Experimental demonstration of tracking two and three moving targets.
    • Achieved average errors of 1.00 and 1.77 macro-pixels for two and three targets, respectively.
    • Operated with a temporal resolution of 0.67 ms using only 12 patterns.

    Conclusions:

    • The developed framework enables simultaneous localization of multiple targets without imaging.
    • The all-optical encoder effectively filters background and irrelevant targets.
    • This work significantly advances single-pixel tracking capabilities for complex scenarios.