KEYWORDS: Sensors, Data modeling, Data transmission, Signal filtering, Sensor networks, Intelligent sensors, Data fusion, Data storage, Data communications, Control systems
The sensor system is crucial to intelligent munitions and must function with incredibly high accuracy and in real time based on the characteristics of the munition. Additionally, it is difficult to meet the requirements of the munition system given the significant temporospatial redundancy of the conventional sensor system. In this paper, a data fusion algorithm based on prediction gray model combined with Kalman Filter (GMKF) is proposed. The algorithm combines the advantages of Kalman Filter in dealing with sensor noise with the advantages of gray model in fast modeling and prediction. The missile model is simulated by Ansys Fluent, and the algorithm performance of the obtained temperature data set is analyzed. The results show that in wireless sensor networks, GMKF algorithm can greatly reduce communication redundancy, increase network lifetime, and has a high prediction success rate.
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