Sparse decomposition of in-air sonar images for object localization

Recently we have developed an in-air sonar system that is capable of generating 3D descriptions of environments containing multiple reflectors using a single broadband emission in combination with a microphone array. We have coined the term “energyscape” for the representation of the environment, i.e. reflected energy as a function of range and direction, generated by this system. While such rich sensor data can prove useful without object segmentation for certain applications, it is sometimes desirable to map the energyscapes onto individual reflector positions. Simple ad-hoc segmentation methods often have problems with the high dynamic range in the sensor data. We propose a method using sparse decomposition of the energyscapes using a dictionary containing direction dependent Point Spread Functions (PSF) of the sensor system. Using an L1-regularized least squares solution method we demonstrate reconstruction of actual measured energyscapes and estimation of reflector positions in complex scenarios.