基于标准数据稀疏采样的传感器校准研究

Sensor Calibration Based on Sparse-sampled Standard Data

为了有效治理水污染,需要加大水质监测工作力度。针对传统水质实验室采样成本高,而实时水质监测系统存在传感器漂移的问题,提出了一种基于实验室标准数据的稀疏采样的校准算法。结合传感器的漂移模型与压缩感知采样理论,采用迭代算法求解传感器漂移和观测矩阵,根据校准误差更新观测矩阵。仿真结果表明,根据提出的稀疏采样策略,以1.2%~2.0%的采样率来采集实验室标准数据,可以有效地校准传感器数据,从而降低人工成本、提高系统准确性。

In order to effectively control water pollution,it is necessary to intensify water quality monitoring.Aiming at the high cost of sampling in traditional water quality laboratory and the problem of sensor drift in real-time water quality monitoring system,a calibration algorithm of sparse sampling based on laboratory standard data was proposed.Based on the sensor drift model and compressed sensing sampling theory,an iterative algorithm is used to solve the sensor drift and observation matrix,and the observation matrix is updated according to the calibration error.The simulation results show that according to the proposed sparse sampling strategy,the standard laboratory data can be collected at a sampling rate of 1.2%~2.0%,which can effectively calibrate sensor data,thus reducing labor cost and improving system accuracy.