Compressed sensing using a non-uniformly sampled range-azimuth dictionary

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 bats external ears. To estimate the range and azimuth of a target, the L1-minimization method is used. Since the high coherence in azimuth templates could cause ambiguity in azimuth estimation, the use of a non-uniform sampled dictionary is investigated. The non-uniform sampling is derived from the coherence between different azimuth templates in the dictionary. 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, both monaural dictionaries have problems interpreting these echo signals correctly. In addition to missing many of the real brick seams they also generate many false reconstructed objects, but constructing a binaural dictionary the results can be improved significantly.