Compressive sensing for WiFi-based passive bistatic radar

Abstract

Passive radars using WiFi base stations as illuminators are a promising option for local area surveillance. Unfortunately, the reflections of WiFi signals from a large object can easily mask any nearby objects making it difficult to detect all targets within range. Using the fact that most target scenes are only very sparsely populated makes it possible to apply compressive sensing (CS) for target detection. We analyze how well WiFi signals fit into the CS framework, propose corresponding detection schemes, and show how detection accuracy is increased and receiver complexity is reduced. It turns out that CS enables the detection of closely spaced targets with only a small number of recorded WiFi frames.