基于卡尔曼滤波的红外发射率测量数据稳定化处理方法研究

Research on a Kalman Filter-Based Data Stabilization Method for Infrared Emissivity Measurement

摘要: 针对手持式红外发射率测量系统中微弱信号易受噪声干扰、测量结果稳定性不足等问题，提出一种基于卡尔曼滤波的发射率测量数据稳定化处理方法。分析探测器噪声、电路噪声、温度漂移及环境热背景扰动对测量结果的影响，结合发射率稳态变化特性，将真实发射率建模为缓慢变化状态量，建立一维随机游走状态空间模型，并采用卡尔曼滤波递推估计实现测量序列优化。在标准镀金发射率样片实验中，采集150组连续测量数据对算法进行验证，并与滑动平均滤波方法进行对比。结果表明，所提方法能够有效抑制随机波动，提高发射率测量稳定性；滤波后最大相对偏差由3.491%降低至1.171%，误差分布更加集中，局部相对标准偏差(RSD)明显降低。该方法无需依赖材料先验发射率信息，可直接应用于嵌入式测量系统，为提升红外发射率现场测量精度与重复性提供有效途径。

Abstract: To address the problems of weak signals being susceptible to noise interference and insufficient measurement stability in handheld infrared emissivity measurement systems, a data stabilization method based on the Kalman filter is proposed for infrared emissivity measurement. The effects of detector noise, circuit noise, temperature drift, and environmental thermal background fluctuations on the measurement results are analyzed. Considering the steady-state variation characteristics of emissivity, the true emissivity is modeled as a slowly varying state variable, and a one-dimensional random-walk state-space model is established. Subsequently, a recursive estimation approach based on the Kalman filter is employed to optimize the emissivity measurement sequence and effectively suppress random fluctuations. Experiments were conducted using a standard gold-coated emissivity reference sample, and 150 consecutive measurement datasets were collected to validate the proposed method. The performance of the proposed approach was compared with that of the conventional moving average filtering method. Experimental results demonstrate that the proposed method effectively suppresses random fluctuations and improves the stability of emissivity measurements. After Kalman filtering, the maximum relative deviation is reduced from 3.491% to 1.171%, with a more concentrated error distribution and a significant reduction in the local relative standard deviation (RSD). The proposed method does not require prior knowledge of the material emissivity and can be directly implemented in the embedded processing unit of the measurement system, providing an effective approach for improving the accuracy and repeatability of in-situ infrared emissivity measurements.