Multidomain Feature-Level Fusion for Space Micromotion Target Recognition Based on Networked Radar Systems

A networked radar system composed of radars deployed in different locations and working in different frequency bands can provide multi-band and multi-view target information, which can effectively improve the recognition accuracy of space micro-motion targets. The feature acquisition ability of each part of the networked radar systems is different. To fully tap the potential information of networked radar systems, a multi-domain feature-level fusion method based on networked radar systems for space micro-motion targets recognition is proposed in this paper. Firstly, the multi-domain features dataset of the networked radar systems is constructed. Secondly, the feature extraction and adaptive fusion subnetwork is designed. Two linear attention modules, cross-covariance atttention (XCA) module and linear angular attention (LA-Att) module, are introduced to extract fine-grained features in parallel and efficiently. The cross-attention fusion module (CAFM) is introduced for adaptive feature fusion. Then, a spatial-temporal feature extraction subnetwork is designed. The temporal features are extracted by bidirectional gated recurrent unit (BiGRU). The spatial dependencies between nodes are captured by the dual graph fusion network (DGFN) which integrates the adaptive graph and the predefined graph. Finally, the spatial-temporal feature vector is input into the prediction layer to obtain robust recognition results. Extensive experiments have proved the effectiveness and robustness of the proposed method.