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Thanks to the definition of the new IEEE 802.11bf standard, the development of Wi-Fi sensing applications is gaining momentum in the research community. In this regard, several studies have shown that learning-based approaches that leverage the frequency response of the Wi-Fi channel in the sub-7 GHz bands can reach high accuracy in different classification tasks, such as activity recognition, or person identification. Instead, more fine-grained applications – e.g., human localization and tracking, or respiration and heartbeat monitoring – require implementing model-based approaches to estimate the Wi-Fi multi-path parameters and analyze the time evolution of the paths associated with specific targets (the human body or chest). In this paper, we investigate the performance of six super-resolution algorithms for sub-7 GHz multi-path parameter estimation. Our extensive evaluation indicates that the estimation …