基于时间序列模型的超声信号滤波算法研究∗

摘要

Aiming at the shortcomings of the complexity and the poor results of the existing filtering algorithm in the oil well Ultrasonic level measurement system,a new method based on time series models and the Innovation-Based Adaptive Kalman Filter is proposed. The ARMA model of the dynamic oil well Ultrasonic level measurement system is established based on the time series model. The Innovation-Based Adaptive Kalman Filter is studied and designed also. Using the online eliminate random noise error characteristics of the Kalman Filter and the characteristics of the ARIMA model can Makes the optimization of the system features state. The method has been used in the producing oil field. The actual test,this method has high accuracy,real time and efficient. And the measurement error is small, which can meet the practical engineering applications.%针对超声波测量油井液面深度系统中，现有降噪滤波算法复杂，效果差的缺点，提出了基于时间序列模型和新息卡尔曼滤波相结合的新方法。利用时间序列分析法对动液面测量系统建立ARMA模型；基于卡尔曼滤波实时在线消除随机噪声的特性，设计了新息自适应卡尔曼滤波算法，并结合ARIMA模型以消除模型误差，实现了基于时间序列系统模型对系统特征状态的最优估计目的。该新型滤波方法已经在油田现场测试和运用，测试结果表明，算法实时、高效，滤波效果好，精度高，能满足实际工程应用。