Tensor-Based Efficient Multi-Interferer RFI Excision: Results Using Real-World Data

Recently, we proposed a multi-linear subspace estimation and projection (MLSEP) algorithm for single-input multiple-output (SIMO) wireless communication systems that may suffer from multiple sources of radio frequency interference (RFI). Simulations corroborate a superior performance for MLSEP over the state-of-the-art projection-based RFI detection algorithms. To assess the efficacy of excision by MLSEP as well as the aforementioned state-of-the-art algorithms, this paper deploys a real-world data contaminated by multiple sources of RFI and conducts a performance assessment on such a data. Lastly, this assessment also demonstrates that MLSEP outperforms projection-based RFI excision algorithms, while effectively extracting the signal of interest from impinging RFIs.