Multiwireless sensors for electrical measurement based on nonlinear improved data fusion algorithm

Abstract

Abstract In order to improve the accuracy of collected data and avoid table lookup, the adaptive weighted fusion algorithm is improved. According to the characteristics of the median and the mean value in the normal distribution, a new method of preprocessing to remove outliers is proposed to improve the accuracy of the final fusion result. The algorithm is used to calculate the temperature data to be processed in a greenhouse. The results showed that the fusion result after average processing was X ˆ \hat{X} = 15.77°C. The standard deviation is σ \sigma = 0.1194°C. After the treatment of the Grabbs criterion, the fusion result is X ˆ \hat{X} = 15.73°C and the standard deviation is σ \sigma = 0.1110°C. The fusion result of the improved algorithm is X ˆ \hat{X} = 15.74°C. The standard deviation is σ \sigma = 0.0959°C. Advantages of various preprocessing algorithms: improved algorithm > Grubbs method > no preprocessing. From the processing results of group A1 data, it can be seen that the improved algorithm can effectively suppress the ipsilateral shielding effect. Compared with the traditional Grubbs method to eliminate outliers and other algorithms, the improved algorithm can make the standard deviation of the fusion result smaller, and the fusion result can better represent the overall distribution, and there is no need to look up the table.