Data fusion method for temperature monitoring of bio-oxidation with wireless sensor networks

Abstract

In the process of bio-oxidation gold extraction located in the cold and high-altitude areas, temperature monitoring in the oxidation tank is noisy and accompanied by measurement losses. A new fusion strategy is proposed for improving the temperature monitoring performance of the bio-oxidation tank. In the specific, the measurement data from the underlying sensors is preprocessed by an improved unscented Kalman filter to reduce the impact of missing data and noises on the fusion method. At the local fusion center, the data is fused by a new covariance intersection algorithm to ensure the consistency and high accuracy of the fusion results. In the global fusion center, all of the cluster head fusion results are fused by a hybrid neural network to improve the accuracy and robustness of the designed fusion algorithm. The simulation shows that the proposed fusion strategy effectively improves the temperature monitoring performance of the oxidation tank.