Reducing false alarm risk in transient signal classification

We address the problem of autonomous decision making in classification of radioastronomy transient signals on spectrograms from spacecraft. It is known that the assessment of the decision process can be divided into acceptance of the classification, instant rejection of the current signal classification, or rejection of the entire classifier model. We propose to combine prediction and classification with a double architecture of neural networks to optimize a decision while minimizing the false alarm risk. We suggest a method to derive the input and output windows for the predictor network. Results on real data from URAP experiment aboard the Ulysses spacecraft show that this scheme is tractable and effective.