| 基于自适应Kalman滤波的SAW测温数据纠错方法 |
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| 引用本文： | 薛明喜,杨扬,张晨睿,韩韬.基于自适应Kalman滤波的SAW测温数据纠错方法[J].仪器仪表学报,2016,37(12):2766-2773. |
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| 作者姓名： | 薛明喜 杨扬 张晨睿 韩韬 |
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| 作者单位： | 上海交通大学电子信息与电气工程学院上海200240,上海交通大学电子信息与电气工程学院上海200240,上海交通大学电子信息与电气工程学院上海200240,上海交通大学电子信息与电气工程学院上海200240 |
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| 基金项目： | 国家自然科学基金(11474203)、国家重点研发计划（2016YFB0402705）项目资助 |
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| 摘 要： | 在无源无线SAW测温系统实际应用中,阅读器接收到的信号往往受到其所处环境电磁波的干扰。这些干扰将会使阅读器得到错误的测量数据。温度变化趋势和测量噪声时变的特点也给系统建模以及噪声估计带来了困难。针对实际应用中存在的问题,在Kalman滤波的基础之上,提出了一种新的自适应算法。该算法采用多项式预测的方法建立温度测量的时变系统模型,根据当前及历史测量值,自行调整预测模型参数,避免因模型不准确造成Kalman滤波效果严重下降的问题;通过对测量数据小波变换的方法,实时估计测量数据噪声方差,克服未知观测噪声的条件下精度下降的问题;当测量数据受到干扰时,测量值与纠错值之间的差值不满足高斯分布,通过对差值统计特性的分析,对测量数据进行错误数据判别与剔除,有效地抑制干扰对温度测量的影响。将这种自适应Kalman滤波算法应用到无源无线SAW测温系统中,无源无线SAW温度传感器测温实验的结果验证了该算法能有效地纠正粗大误差,提高测量系统的精度。
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| 关 键 词： | Kalman滤波 多项式预测 小波变换 粗大误差 残差 |
Error correction method for SAW temperature measurement data based on adaptive Kalman filter |
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| Xue Mingxi,Yang Yang,Zhang Chenrui and Han Tao.Error correction method for SAW temperature measurement data based on adaptive Kalman filter[J].Chinese Journal of Scientific Instrument,2016,37(12):2766-2773. |
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| Authors: | Xue Mingxi Yang Yang Zhang Chenrui and Han Tao |
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| Affiliation: | School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China,School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China,School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China and School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China |
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| Abstract: | In the practical application of passive wireless SAW temperature measurement system, the signal received by the reader is often interfered by the electromagnetic waves in the environment where the reader is in. These interferences will make the reader get wrong measurement data. The temperature changing trend and the time varying characteristic of themeasurement noise also bring difficulty to the system modeling and noise estimation.Aiming at these problems existing in practical application, on the basis of Kalman filter, this paper proposes a new adaptive algorithm, which is fault tolerant to outliers. The algorithm adopts polynomial prediction method to establish the time variant system model of temperature measurement. According to current and historical measurement data, the new algorithm can self adjust the forecasting model parameters and avoid the problem that the performance of Kalman filter severely degrades because of inaccurate model.The wavelet transformof the measurement data is used to estimate the noise variance of the measurement data in real time and overcome the problem that the accuracy degrades due to unknown measurement noise. When the measurement data are interfered the outliers occur, the difference between the measured value and corrected value does not obey the Gaussian distribution any more; through analyzing the statistical characteristics of the difference value, discriminating and rejecting the error data from the measurement data, the influence of the interference on temperature measurement is effectively suppressed and the precision of the system is improved.Finally, the proposed adaptive Kalman filter algorithm was used in the passive wireless SAW temperature measurement system.The results of temperature measurement experiment using the passive wireless SAW temperature sensor verify that the proposed algorithm can effectively correct the outliers and improve the system precision. |
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| Keywords: | Kalman filter polynomial prediction wavelet transform outlier residuum |
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