Near-field source localization based on sparse reconstruction of sensor-angle distributions

In this paper, we consider a problem of near-field source localization using the sensor-angle distribution (SAD) that views the source range and direction-of-arrival (DOA) information as sensor-dependent phase progression. The SAD draws parallel to quadratic time-frequency distributions and, as such, is able to reveal the changes in the spatial frequency over sensor positions. In particular, for a moderate source range, the SAD signature is of polynomial shape, thus simplifying the parameter estimation. We consider sparse arrays where the array sensors are located on a grid but with missing positions. Sparse reconstruction techniques are used to estimate the SAD in the joint space and spatial frequency domain, and the results are then mapped back to source range and DOA estimation for source localization. The effectiveness of the proposed technique is verified using simulation results.