Kalman filtering used for passive synthetic aperture

In practice, beamforming-based bearing estimators typically fail to account for the impact of environmental factors on the estimating results, resulting in the so-called mismatch problem. To deal with this issue, this paper reconstructs the bearing estimation as a Kalman filter problem, which emphasizes the role of physical models in underwater acoustic signal processing. Since passive synthetic aperture (PSA) is a spatial process including the temporal evolution of a moving array, it fits a Kalman filtering structure. Kalman filter is applied directly to a lake-test dataset collected by an autonomous underwater vehicle (AUV) shell-mounted array, and the experimental findings demonstrate that the bearing estimation results’ variation is significantly decreased than the conventional beamforming (CBF).