Multimodal Sparse Representation-Based Classification Scheme for RF Fingerprinting

In this letter, we propose a multimodal method for improving radio frequency (RF) fingerprinting performance that uses multiple features cultivated from RF signals. Combining multiple features, including a falling transient feature that has not previously been used in RF fingerprinting studies, we aim to demonstrate that the proposed method results in improved accuracy. We show that a sparse representation-based classification (SRC) scheme can be a good platform for combining multiple features. The experimental results on RF signals acquired from eight walkie-talkies show that the RF fingerprinting accuracy of the proposed method improves significantly as the number of features increases.