New Fractional Ambiguity Function Integrated with CNN-Based Machine Learning for Signal Classification
arXiv:2606.08110v1 Announce Type: cross Abstract: A new fractional ambiguity function (NFrAF) derived from the fractional Fourier transform is introduced as a generalization of the classical ambiguity function. The fundamental analytical properties of the NFrAF, including symmetry, marginality, and Moyal type identities, are rigorously established. After verifying its ability to detect and localize monocomponent and multicomponent linear frequency modulated (LFM) signals, the NFrAF is integrated into a convolutional neural network based machine learning framework for signal classification. Owi
The continuous advancements in AI and machine learning, particularly in deep learning architectures like CNNs, enable more sophisticated signal processing techniques at this moment.
This research introduces a novel, more generalized mathematical tool for signal analysis, directly integrating it with AI, which can significantly enhance signal classification capabilities across various applications.
The ability to accurately detect and classify complex signals, especially LFM signals, improves, potentially leading to more robust and higher-performing signal processing systems through this new mathematical framework.
- · Defence sector
- · Telecommunications
- · AI researchers
- · Signal processing engineers
- · Legacy signal processing methods
- · Systems reliant on less robust signal analysis
Improved signal classification accuracy using fractional ambiguity functions integrated with CNNs for advanced applications.
Enhanced capabilities in areas like radar, sonar, and communication systems due to superior signal detection and analysis.
New developments in autonomous systems and surveillance that rely heavily on precise and robust signal interpretation.
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Read at arXiv cs.LG