Peering through a Dirty Window: A Bayesian Approach to Making Mine Detection Decisions from Noisy Data.

摘要

For several reasons, Bayesian parameter estimation is superior to other methods for extracting features of a weak signal from noise. Since it exploits prior knowledge, the analysis begins from a more advantageous starting point than other methods. Also, since nuisance parameters can be dropped out of the Bayesian analysis, the description of the model need not be as complete as is necessary for such methods as matched filtering. In the limit for perfectly random noise and a perfect description of the model, the signal-to-noise ratio improves as the square root of the niunber of samples in the data. Even with the imperfections of real-world, Bayesian approaches this ideal limit of performance more closely than other methods. A major unsolved problem in landrnine detection is the fision of data from multipie sensor types. Bayesian data fusion is only beginning to be explored as a solution to the problem. In single sensor processes Bayesian analysis can sense multiple parameterd from the data stream of the one sensor.