多传感器数据的处理及融合

针对多传感器数据的多样性, 提出一种改进的数据融合算法. 首先, 利用小波技术消除已收集数据的高斯白噪声并对数据进行压缩； 其次, 对处理后的数据进行分层, 并对系数进行Kalman滤波, 同时利用Mallat快速重建算法重构数据； 最后, 利用最大、 最小贴近度计算传感器数据的信噪比, 并通过信噪比进行数据融合. 基于实际采集的多传感器数据对比实验结果表明, 该数据融合算法在稳定性上优于简单加权数据融合、 小波数据融合和Kalman滤波融合等算法.

Processing and Fusion for Multi sensor Data

Aiming at the diversity of multi sensor data, we proposed an improved data fusion algorithm. Firstly, the wavelet technology was used to eliminate the Gaussian white noise of collected data and compress the data. Secondly,the processed data was stratified, and the coefficients were filtered by Kalman. Meanwhile, the data was reconstructedby the Mallat fast reconstruction algorithm. Finally, the signal to noise ratio (SNR) was calculated bymaximum and minimum degree of close to the sensor data, and data fusion was carried out by SNR. Experimental results based on multi sensor actual data show that the data fusion algorithm is superior to simple weighted data fusion, wavelet data fusion and Kalman filtering fusion algorithm in stability.