Yi, Q.; Wang, Q.; Zhang, J.; Zheng, X.; Lu, Z. Fine-Grained Recognition of Mixed Signals with Geometry Coordinate Attention. Sensors2024, 24, 4530.
Yi, Q.; Wang, Q.; Zhang, J.; Zheng, X.; Lu, Z. Fine-Grained Recognition of Mixed Signals with Geometry Coordinate Attention. Sensors 2024, 24, 4530.
Yi, Q.; Wang, Q.; Zhang, J.; Zheng, X.; Lu, Z. Fine-Grained Recognition of Mixed Signals with Geometry Coordinate Attention. Sensors2024, 24, 4530.
Yi, Q.; Wang, Q.; Zhang, J.; Zheng, X.; Lu, Z. Fine-Grained Recognition of Mixed Signals with Geometry Coordinate Attention. Sensors 2024, 24, 4530.
Abstract
With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.
Keywords
Machine learning; Fine-grained image recognition; Residual neural network; Information geometry
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
