Fusing sensors with uncertain detection performance

Sensor fusion is the notion of combining the data from two or more sensors in order to obtain enhanced performance compared with that of the individual sensors. In addition, Signal Detection Theory can be used to monitor how well a sensor operates. That is, through the number of hits, misses, false alarms and correct rejections a sensor registers, we gain a better understanding as to how reliably it performs. Typically, the performance of a sensor is given in terms of its probability of detection and probability of false alarm, which may not be well characterised. In this paper, we use the Transferable Belief Model to fuse two sensors where there is uncertainty in their performance, so that if two sensors give a report, for example, we can estimate the likelihood of the target being present. We also show that when we have known prior probabilities our result is equivalent to the Bayesian case. A numerical example, as well as entropy measures, are also discussed.