Direction-of-arrival estimation via Khatri-Rao subspace using compressed sensing
Mukumoto Hirotaka, Kazunori Hayashi, Megumi Kaneko · 2016
Direction-of-arrival (DOA) estimation via Khatri-Rao (KR) subspace with multiple signal classification (MUSIC) algorithm can cope with a higher number of incoming waves than that of sensors, while it requires the signals to be quasi-stationary and needs a larger number of “frames” than that of incoming waves. On the other hand, a hybrid approach of compressed sensing and MUSIC algorithm can estimate DOAs with snapshots less than the number of sources for noiseless observation, although the number of incoming waves must be less than that of sensors. Exploiting the fact that the frame in MUSIC via KR subspace corresponds to the snapshot in conventional MUSIC without observation noise, we propose a DOA estimation scheme using KR product array processing and compressed sensing, which can cope with a greater number of incoming waves than both that of sensors and that of frames. The validity of the proposed method is shown via numerical experiments.