The Laser Light Screen System faces critical technical challenges in high-speed, long-range target detection: when a target passes through the light screen, weak light flux variations lead to significantly degraded signal-to-noise ratios (SNRs). Traditional signal processing algorithms fail to effectively suppress phase distortion and boundary effects under extremely low SNR conditions, creating a technical bottleneck that severely constrains system detection performance. To address this problem, this paper proposes a Multi-stage Collaborative Filtering Chain (MCFC) signal processing framework incorporating three key innovations: (1) the design of zero-phase FIR bandpass filtering with forward-backward processing and dynamic phase compensation mechanisms to effectively suppress phase distortion; (2) the implementation of a four-stage cascaded collaborative filtering strategy, combining adaptive sampling and anti-aliasing techniques to significantly enhance signal quality; and (3) the development of a multi-scale adaptive transform algorithm based on fourth-order Daubechies wavelets to achieve high-precision signal reconstruction. The experimental results demonstrate that under -20 dB conditions, the method achieves a 25 dB SNR improvement and boundary artifact suppression while reducing the processing time from 0.42 to 0.04 s . These results validate the proposed method’s effectiveness in high-speed target detection under low SNR conditions. 激光光幕系统在高速、远距离目标检测方面面临关键的技术挑战：当目标穿过光幕时，微弱的光通量变化会导致信噪比 （SNR） 显着下降。传统的信号处理算法在极低的 SNR 条件下无法有效抑制相位失真和边界效应，形成了严重制约系统检测性能的技术瓶颈。针对这一问题，本文提出了一种多级协同滤波链（MCFC）信号处理框架，该框架融合了三个关键创新：（1）设计了具有正向后处理和动态相位补偿机制的零相位 FIR 带通滤波，以有效抑制相位失真;（2） 实施四级级联协同滤波策略，结合自适应采样和抗混叠技术，显著提高信号质量;（3） 开发基于四阶 Daubechies 小波的多尺度自适应变换算法，以实现高精度信号重建。实验结果表明，在 -20 dB 条件下，该方法实现了 25 dB 的 SNR 改进和边界伪影抑制，同时将处理时间从 0.42 s 缩短到 0.04 s。这些结果验证了所提方法在低 SNR 条件下高速目标检测中的有效性。
The precise measurement of dynamic parameters such as velocity, trajectory, and spatial position not only represents a fundamental requirement across numerous scientific and engineering domains but has also driven the rapid development of photoelectric detection technologies. Among these, Laser Light Screen Systems (LLSSs) have gained widespread attention in dynamic testing applications due to their significant advantages, including non-contact operation, high responsivity, and cost-effectiveness [1-4]. LLSSs function by generating structured light fields within a measurement volume; when a target traverses this field, it reflects laser radiation that is subsequently captured by strategically placed detection screens equipped with photoelectric sensors [5,6]. These sensors convert the intercepted light into weak electrical signals that encode critical information about the target’s dynamics [6]. By employing multiple detection screens with known positions and orientations, LLSSs enable the reconstruction of high-speed target trajectories through spatial parameter analysis and precise timing of screen passages [7,8], proving invaluable in applications like ballistics testing and engineering safety assessments [7,9,10]. 精确测量速度、轨迹和空间位置等动态参数不仅代表了众多科学和工程领域的基本要求，而且还推动了光电探测技术的快速发展。其中，激光屏幕系统 （LLSS） 因其非接触式作、高响应性和成本效益等显著优势而在动态测试应用中受到广泛关注 [1-4]。LLSS 通过在测量体积内生成结构光场来发挥作用;当目标穿过该区域时，它会反射激光辐射，随后由配备光电传感器的战略性放置的检测屏幕捕获[5,6]。这些传感器将截获的光转化为微弱的电信号，这些电信号编码有关目标动力学的关键信息 [6]。通过采用具有已知位置和方向的多个检测屏幕，LLSS 可以通过空间参数分析和屏幕通道的精确定时来重建高速目标轨迹[7,8]，在弹道测试和工程安全评估等应用中被证明是无价的[7,9,10]。
However, the practical application of LLSSs faces significant challenges, primarily related to signal integrity. As the detection distance increases, the reflected energy attenuates sharply, compounded by the spatial sensitivity characteristics of the detectors, leading to a rapid degradation of the signal-to-noise ratio (SNR) [11-13]. Furthermore, the resulting photoelectric signals often exhibit complex characteristics, including nonlinearity arising from detector spatial sensitivity, non-periodicity due to the random nature of target passage, and non-stationarity (time-varying statistical properties), which becomes particularly acute when the SNR falls below critical thresholds (e.g., 5 dB ) [13]. These inherent signal degradations severely compromise measurement accuracy and reliability, necessitating the development of sophisticated signal processing techniques capable of extracting meaningful information from these weak and complex waveforms [3]. 然而，LLSS 的实际应用面临重大挑战，主要与信号完整性有关。随着探测距离的增加，反射能量急剧衰减，再加上探测器的空间灵敏度特性，导致信噪比（SNR）迅速下降[11-13]。此外，产生的光电信号通常表现出复杂的特性，包括探测器空间敏感性引起的非线性、目标通过随机性引起的非周期性以及非平稳性（时变统计特性），当 SNR 低于临界阈值（例如 5 dB）时，这种情况会变得尤为严重 [13]。这些固有的信号衰减严重损害了测量的准确性和可靠性，因此需要开发复杂的信号处理技术，以便能够从这些微弱而复杂的波形中提取有意义的信息 [3]。
Historically, efforts to address these signal processing challenges have evolved through distinct stages. Initial approaches relied heavily on frequency-domain analysis, such as the Fourier transform. While offering spectral insights for periodic signals, these methods struggle with the non-stationary nature of LLSS signals, often introducing artifacts like spectral leakage due to inherent stationarity assumptions [14]. Wavelet transforms emerged as an improvement, providing multi-resolution analysis capabilities [15,16]. However, their efficacy diminishes in extremely low SNR scenarios ( < -10dB<-10 \mathrm{~dB} ), suffering from difficulties in optimal parameter selection and pronounced boundary effects that distort finite-length signals [17]. 从历史上看，解决这些信号处理挑战的努力经历了不同的阶段。最初的方法在很大程度上依赖于频域分析，例如傅里叶变换。虽然这些方法为周期性信号提供了频谱洞察，但这些方法难以解决 LLSS 信号的非平稳性质，由于固有的平稳性假设，通常会引入频谱泄漏等伪影 [14]。小波变换是一种改进，提供了多分辨率分析能力[15,16]。然而，在极低 SNR 的情况下 （ < -10dB<-10 \mathrm{~dB} ），它们的功效会降低，难以选择最佳参数，并且存在明显的边界效应，使有限长度信号失真 [17]。
Recognizing the limitations of frequency-domain methods for non-stationary and nonlinear signals, time-frequency joint analysis techniques were developed. Empirical Mode Decomposition (EMD) was introduced to adaptively decompose signals but is plagued by mode mixing issues [18]. While Ensemble EMD (EEMD) alleviates mode mixing, it introduces phase distortions and significantly increases computational demands [19]. Variational Mode Decomposition (VMD) represents a more mathematically grounded advancement, demonstrating success in various fields [20-23]. Variational Mode Decomposition (VMD) offers superior noise robustness compared to Empirical Mode Decomposition (EMD) and its ensemble variant (EEMD); however, its efficacy is critically dependent on the pre-selection of key parameters-namely the mode number KK and penalty factor alpha\alpha-and its convergence can deteriorate under low signal-to-noise ratio (SNR) conditions. Notably, the reported precision gains in underwater laser ranging via VMD-ICA integration were realized only through meticulous parameter tuning [23]. 认识到频域方法对非平稳和非线性信号的局限性，开发了时频联合分析技术。经验模态分解 （EMD） 被引入以自适应方式分解信号，但受到模态混合问题的困扰 [18]。虽然 Ensemble EMD （EEMD） 减轻了模式混频，但它引入了相位失真并显著增加了计算需求 [19]。变分模态分解 （VMD） 代表了一种更具数学基础的进步，在各个领域都取得了成功 [20-23]。与经验模态分解 （EMD） 及其集成变体 （EEMD） 相比，变分模态分解 （VMD） 提供了卓越的噪声鲁棒性;然而，其有效性在很大程度上取决于关键参数（即模式数 KK 和惩罚因子 alpha\alpha ）的预选，并且在低信噪比 （SNR） 条件下其收敛性可能会恶化。值得注意的是，据报道，通过 VMD-ICA 集成实现水下激光测距的精度增益是通过细致的参数调整来实现的 [23]。
More recently, the surge in computational power has spurred the development of data-driven approaches, including deep learning, for weak signal processing [24-27]. These methods can achieve remarkable noise suppression and feature extraction capabilities. Nevertheless, their application to LLSSs is often constrained by the need for large, representative training datasets, challenges in ensuring real-time performance crucial for high-speed measurements, limited interpretability of complex models, and potential difficulties in generalizing to diverse operational conditions without integrating underlying physical principles. 最近，计算能力的激增刺激了用于弱信号处理的数据驱动方法的发展，包括深度学习 [24-27]。这些方法可以实现出色的噪声抑制和特征提取功能。然而，它们在 LLSS 中的应用通常受到以下因素的限制：需要大型、有代表性的训练数据集、确保对高速测量至关重要的实时性能的挑战、复杂模型的有限可解释性以及在不集成基本物理原理的情况下推广到不同作条件的潜在困难。
Collectively, these existing methodologies face several critical limitations when applied to the demanding context of LLSS weak signal processing: (1) a fundamental difficulty in preserving temporal signal integrity under extremely low SNR conditions (e.g., below -10 dB ); (2) inadequate real-time processing capabilities for high-speed targets; (3) overreliance on data without sufficient integration of physical signal characteristics; (4) poor interpretability of ‘black-box’ models; (5) inherent assumptions of stationarity or linearity that fail to capture the time-varying nature of the signals; and (6) boundary effects in finitelength signal processing leading to energy leakage and distortion. These shortcomings 总的来说，这些现有方法在应用于 LLSS 弱信号处理的苛刻环境时面临几个关键限制：（1） 在极低的 SNR 条件下（例如，低于 -10 dB）保持时间信号完整性存在根本困难;（2） 对高速目标的实时处理能力不足;（3） 过度依赖数据而没有充分整合物理信号特性;（4） “黑盒”模型的可解释性差;（5） 无法捕捉信号时变性质的稳态性或线性性的固有假设;（6） 有限长度信号处理中的边界效应导致能量泄漏和失真。这些缺点
collectively hinder the effective operational range and reliability of LLSSs, particularly as target distance increases or ambient noise intensifies. 共同阻碍了 LLSS 的有效工作范围和可靠性，尤其是在目标距离增加或环境噪声加剧时。
To overcome these limitations, this paper introduces a novel Multi-stage Collaborative Filtering Chain (MCFC) framework specifically designed for robust processing of weak photoelectric signals from the LLSS. The MCFC framework uniquely integrates adaptive bidirectional processing, a multi-stage cascaded filtering strategy, and sophisticated non-stationary signal analysis techniques. This synergistic approach aims to significantly enhance the SNR while meticulously preserving the crucial temporal characteristics embedded within the signal, thereby enabling reliable long-range, high-speed target detection even in complex and noisy environments. We posit that this framework is particularly advantageous for high-precision measurement applications demanding high fidelity, such as ballistics analysis, collision studies, and structural health monitoring. The core contributions of this work, demonstrating the advantages of the MCFC over conventional methods, are as follows: 为了克服这些限制，本文引入了一种新的多级协同滤波链 （MCFC） 框架，专门用于对来自 LLSS 的弱光电信号进行鲁棒处理。MCFC 框架独特地集成了自适应双向处理、多级级联滤波策略和复杂的非平稳信号分析技术。这种协同方法旨在显著提高 SNR，同时精心保留信号中嵌入的关键时间特性，从而即使在复杂和嘈杂的环境中也能实现可靠的长距离、高速目标检测。我们认为该框架特别有利于需要高保真度的高精度测量应用，例如弹道分析、碰撞研究和结构健康监测。这项工作的核心贡献，展示了 MCFC 相对于传统方法的优势，如下：
Zero-phase FIR Bandpass Filtering Mechanism: A novel filtering approach employing forward-backward processing combined with dynamic phase compensation to rigorously suppress phase distortion, ensuring high-fidelity signal representation throughout the processing chain. 零相位 FIR 带通滤波机制：一种新颖的滤波方法，采用前向后处理与动态相位补偿相结合，严格抑制相位失真，确保整个处理链中的高保真信号表示。
Four-stage Cascaded Collaborative Filtering Strategy: An adaptive, multi-stage filtering architecture that synergistically integrates adaptive sampling and sophisticated anti-aliasing techniques. This strategy optimally balances signal reconstruction fidelity, smoothness, and parameter sparsity for enhanced signal quality. 四级级联协同滤波策略：一种自适应、多级滤波架构，协同集成自适应采样和复杂的抗锯齿技术。此策略以最佳方式平衡信号重建保真度、平滑度和参数稀疏性，以提高信号质量。
Multi-scale Adaptive Transform Algorithm: A robust adaptive transformation algorithm, based on the fourth-order Daubechies wavelet, designed to achieve highprecision signal reconstruction while maintaining stability and effectiveness across diverse and challenging noise environments. 多尺度自适应变换算法：一种强大的自适应变换算法，基于四阶 Daubechies 小波，旨在实现高精度信号重建，同时在各种具有挑战性的噪声环境中保持稳定性和有效性。
Significant Performance Enhancement and Empirical Validation: Rigorous experimental evaluations show that the proposed MCFC framework achieves a remarkable SNR improvement of up to 45 dB for input signals with an SNR as low as -15 dB . Furthermore, correlation coefficients consistently exceeding 0.98 are maintained across various noise conditions, demonstrating superior performance and stability compared to traditional methods. 显著的性能增强和实证验证：严格的实验评估表明，对于信噪比低至 -15 dB 的输入信号，所提出的 MCFC 框架实现了高达 45 dB 的显著 SNR 改进。此外，在各种噪声条件下，相关系数始终保持在 0.98 以上，与传统方法相比，表现出卓越的性能和稳定性。
By addressing the critical bottlenecks in current weak signal processing techniques, the MCFC framework provides an effective solution for photoelectric signal processing and offers a reliable approach for high-precision measurement applications. 通过解决当前弱信号处理技术中的关键瓶颈，MCFC 框架为光电信号处理提供了有效的解决方案，并为高精度测量应用提供了一种可靠的方法。
The structure of this article is as follows: Section 2 covers the MCFC framework’s theory and implementation, including bidirectional filtering models and convergence analysis. Section 3 presents experimental validation and comparisons with state-of-the-art methods. Section 4 discusses system performance and implementation, while Section 5 outlines future research directions. Figure 1 illustrates the signal acquisition and processing scheme, emphasizing target detection under severe noise interference. 本文的结构如下：第 2 节介绍了 MCFC 框架的理论和实现，包括双向过滤模型和收敛分析。第 3 节介绍了实验验证和与最先进方法的比较。第 4 节讨论了系统性能和实现，而第 5 节概述了未来的研究方向。图 1 说明了信号采集和处理方案，强调在严重噪声干扰下进行目标检测。
Figure 1 illustrates the signal acquisition and processing workflow of the LLSS: In Figure 1a, this system integrates a line-type semiconductor infrared laser as an active illumination source into the conventional light screen architecture. The system incorporates optical lenses, slit apertures, and photoelectric detection devices to construct the detection unit, forming a light screen and a laser light screen. When a target goes through the light screen, laser energy reflected from its surface is captured by the photoelectric detection devices through the optical system and converted into electrical signals. Figure 1b depicts the time-domain signal recorded under normal signal-to-noise ratio (SNR) conditions, where the target-induced pulse appears prominently above a low-level noise floor (hori- 图 1 说明了 LLSS 的信号采集和处理工作流程：在图 1a 中，该系统将线型半导体红外激光器作为主动照明源集成到传统的光幕架构中。该系统结合了光学透镜、狭缝孔径和光电检测装置来构建检测单元，形成光幕和激光光幕。当目标物穿过光幕时，从其表面反射的激光能量被光电检测装置通过光学系统捕获并转换为电信号。图 1b 描述了在正常信噪比 （SNR） 条件下记录的时域信号，其中目标感应脉冲明显高于低电平本底噪声 （hori-