Multi-sensor Data Fusion Algorithm Based on Adaptive Trust Estimation and Neural Network - Zhao, Xuexin; Wu, Junhua; Wang, Maoli; Li, Guangshun; Yu, Haili; Feng, Wenzhen (2020 IEEE/CIC International Conference on Communications in China (ICCC), 2020)
Multi-sensor data fusion technique plays a key role in the agricultural services such as data collection and processing. However, the collected data usually is featured by redundancies and errors, which deteriorate the reliability of network. In this paper, based on adaptive trust estimation, we propose a multisensor data fusion algorithm in Trust Neural Network (T-NN), aiming to solve the problem of low accuracy and poor stability of multi-sensor data fusion. In particular, the original data collected by the sensors first are pre-processed by exponential smoothing. Then, trust estimation model is applied to calculate the value of trust among the sensing nodes and optimize the data, and the performance of redundancy and reliability are enhanced. Furthermore, the data optimized is introduced into BP neural network for training and fusion. Extensive simulations show that the algorithm proposed in this paper greatly outperforms adaptive weighted average model and traditional BPNN model, in terms of the accuracy of data fusion.