Inclusive Consistency-Based Quantitative Decision-Making Framework for Incremental Automatic Target Recognition

When new unknown samples are captured continually in the open-world environment, the concept diversity accumulation of existing classes and the identification/creation of new concept classes should be considered simultaneously. Since the initial training set of existent classes may be underprepared, adhering to immediate decisions will inevitably lead to reduced open-set recognition (OSR) performance and higher costs of labeling/updating. Inspired by quantitative indicators in predictive reliability assessment and semisupervised/active learning, the inclusive-consistency-based quantitative decision-making (ICQdm) framework is proposed for incremental automatic target recognition (ATR) to evaluate the identifiability and typicality of new unknown samples, which could give the decision-making guide of recognition and updating. For recognition decision-making, the first consistency indicator calculates the reliability of the unknown sample being included by one specific training class. The test samples with low reliability should be the wrongly classified samples and new unknown classes' samples, which are difficult to be labeled by recognition models themselves. For updating decision-making, the second consistency indicator is designed to be the sample distribution density under the inclusive constraint, which could highlight the dense sample distributions of new unknown samples outside the known training distribution. Experiments verify that the proposed ICQdm outperforms other comparison methods on the OSR reliability evaluation and labeling/updating efficiency.