Indoor Temperature Data Fusion Based On Improved Kalman Algorithm
For the problems of complex internal space structure of traditional buildings,high wiring cost and low data acquisition accuracy,LoRa wireless communication technology is used to build a sensor network,which is mainly used to monitor the changes of indoor temperature parameters;For the phenomenon of small fluctuations in traditional Kalman data fusion results,the isolated forest algorithm is introduced,an indoor temperature data fusion algorithm based on the improved Kalman filter algorithm is proposed.The collected data is randomly added to the perturbed samples and distorted data,the generation errors of three algorithms are compared,The error range of the improved Kalman data fusion algorithm is controlled between-0.12 and 0.1 with the perturbed samples and from-0.03 to 0.14 with the distorted data,the improved Kalman data fusion algorithm is much smaller than the traditional Kalman data fusion algorithm and average algorithm.The experimental simulation results show that the improved algorithm increases the ro-bustness and accuracy of indoor temperature data acquisition.
万方数据
维普
