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Dual-optimized adaptive Kalman filtering algorithm based on BP neural network and variance compensation for laser

Sheng Zhou, Chong-Yang Shen, Lei Zhang

    Optics Express
    |November 6, 2019
    PubMed
    Summary

    A novel dual-optimized adaptive Kalman filtering (DO-AKF) algorithm enhances trace gas detection sensitivity. This advanced method improves laser spectroscopy for applications like exhaled CO analysis.

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    Area of Science:

    • Laser Spectroscopy
    • Signal Processing
    • Analytical Chemistry

    Background:

    • Accurate trace gas detection is crucial for environmental monitoring and medical diagnostics.
    • Traditional methods often struggle with sensitivity and parameter variations in dynamic systems.
    • Adaptive Kalman filtering offers potential but requires robust parameter optimization.

    Purpose of the Study:

    • To develop a highly sensitive trace gas detection algorithm for laser spectroscopy.
    • To improve Kalman filter (KF) performance using neural networks and variance compensation.
    • To validate the algorithm's efficacy in a real-world gas sensing application.

    Main Methods:

    • Developed a dual-optimized adaptive Kalman filtering (DO-AKF) algorithm.
    • Integrated a back propagation (BP) neural network for KF parameter optimization.
    • Incorporated variance compensation to manage dynamic system parameter variations.
    • Compared DO-AKF against traditional multi-signal average, extended KF, unscented KF, BP-KF, and VC-KF.

    Main Results:

    • The DO-AKF algorithm demonstrated superior performance over all compared methods.
    • Applied to a QCL-based sensor for exhaled CO analysis, it achieved a 23-fold sensitivity enhancement.
    • Effectively tracked system states and eliminated parameter variations in dynamic systems.

    Conclusions:

    • The DO-AKF algorithm significantly enhances sensitivity in laser spectroscopy for trace gas detection.
    • This method offers a robust solution for real-time monitoring in diverse fields.
    • Potential applications include environmental pollutant monitoring, industrial process control, and breath gas diagnosis.