C41. Multisensor estimate fusion based on Bayesian minimum mean square error criterion

Estimate fusion combines data from distributed sensors, monitor an entity, to obtain an accurate estimate of the entity's. It takes advantages of redundancy and diversity present in the measured data. If the measured data from multiple sensors is correctly combined, then the fused sensor data has better accuracy. This paper addresses the problem of estimate fusion, based on Bayesian minimum mean square error criterion, using measurement from multiple sensors. The results show that the performance of the fused measurements may perform worse than the performance of the individual sensor measurements. The best performance of the fused measurements occurs when the sensors have the same accuracies. The performance of the fused measurements is worse than the performance of the best accurate sensor when the sensors' accuracies vary widely. In this case, adopting the best accurate sensor is recommenced and estimate fusion is not recommended.