Abstract: To address the challenges of difficult aggregation of multi-source sensing information,severe observation missingness,and insufficient interpretability of existing deep multimodal fusion models in complex naval battlefield environments,this paper proposes a ship recognition method based on distributed information-granule and rule construction(DIRC).The proposed method employs information granules as structured carriers shared across distributed nodes and achieves semantic alignment of locally incomplete views through federated consensus constraints without exposing raw data.On this basis,a Takagi-Sugeno(TS) fuzzy rule classifier is constructed for ship recognition,enabling interpretable collaborative recognition under distributed conditions.In addition,an adaptive weighted aggregation mechanism is designed to alleviate aggregation bias caused by data heterogeneity.Experimental results on the MyFleet dataset generated from the “Lingyi” wargame simulation system demonstrate that the proposed method maintains favorable recognition performance under high missing-rate conditions,achieving an accuracy of 70.42% at a missing rate of 75%,while also producing recognition rules with explicit physical meanings and tactical implications.The proposed method provides an interpretable and robust technical solution for ship target recognition and decision support under constrained communication and fragmented information conditions.

Key words: Ship recognition, Federated learning, Distributed incomplete data, Takagi-Sugeno fuzzy rules, Interpretability