Interpretable hierarchical error correction GRU model for effective observation selection

Abstract

Interpretability of extended target effective observation selection model in artificial intelligence (AI) symbolizes a severe challenge: both accuracy effective observation selection while ensuring real-time performance with an extended target multiple observation model and physical interpretability are vital. To meet the challenge, this paper proposes a novel hierarchical Gated Recurrent Unit (GRU) interpretable model. The contribution of this study lies in filling the research gap in the field of effective observation processing for extended targets and intuitively explaining the spatiotemporal visualization of the model. Compared with other interpretable models, the proposed model has improved data clustering and feature extraction capabilities, noise robustness, and computational resource consumption in the face of complex extended target multivariate physical factors. More importantly, interpretable studies are presented on the spatiotemporal characteristics of the multiple observation model of the extended target. The experimental results in real-world scenarios demonstrate that the proposed model is better than benchmark models in terms of prediction performance. The visualization and interpretation of the integrated predictive model reflects the reasonability of the proposed ensemble model.