Biological sensor performance validation using fusion technique

Sensors are used for providing a system with needed data considering some features of interest in the environment of system. Multi-sensor fusion would provide more accurate and reliable information. Multi-sensor fusion would be beneficial in numerous ways such as timeliness, redundancy, complementarily and so on. The main purpose of the research is to examine the biological sensor performance validation using data fusion technique. The fusion or integration of simulated sensor would minimize overall uncertainty and thus helps to maximize the accuracy. It would provide redundant data and also serve to maximize reliability in terms of sensor failure or error. The implementation would be performed in two phases such as data fusion approach and neural network approach. The code would be executed in the MATLAB. Glucose sensor and sucrose sensor were used as the biological sensor. Fusion method used is the state-vector fusion method and a Kalman filter and H-infinity based filter are implemented for enhancing the performance of data fusion algorithm. Simulate the sensor network and deploy the algorithm of data fusion and use neural network for validating the faulty of the sensor network. From the analysis, it was noticed that when compared to simulated stated sensor output, the simulated fused sensor output and target performs well. It was also observed that error rate also minimal in the simulated fused sensor. Further, Future work would be based on validating the biological and cognitive sensor performance through other fusion models.