Compressed Sensing of Complex Reflections Using Range-Azimuth Dictionary in a Bionic Sonar System

The FM-bats are known to be able to sense the environment by echolocation. In this paper, assuming the objects in the environment can be characterized by a sparse representation of the echoes in range and azimuth, a compressed sensing algorithm using a range-azimuth dictionary is proposed. The monaural and binaural range-azimuth dictionaries are constructed from measurements collected with a bionic sonar system consisting of one emitter and two receivers fitted with a 3-D printed replica of a real bat’s external ears. To estimate the range and azimuth of a target, the L1-minimization method is used. Since the high coherence in templates could cause ambiguity, the non-uniform sampled dictionary derived from the coherence is investigated. The non-uniformly sampled monaural and binaural dictionaries are used to process the echoes collected from a real brick-wall. Results indicate that strong echoes can be correctly localized both in azimuth and range by all three dictionaries, but for weak, highly overlapping echoes, monaural dictionaries have problems interpreting these echo signals correctly while binaural dictionary could improve the result.