Adaptive filter design for suppressing the moving surface interferences in optic-acoustic remote sensing

Optic-acoustic remote sensing has been proposed to detect underwater sound signal from the air, thus providing a new method for underwater communication and detection. It exploits the fact that acoustic pressure waves propagating within the water environment cause vibrations on an air-water interface and the particle velocities can be optically detected by observing the amplitude or phase characteristics of the reflected optical beam which is emanated from a laser interferometer. This paper studies an interesting phenomenon observed from our optic-acoustic remote sensing experiments. In the hydrodynamic surface condition, the air-water interface is in motion, thus the moving water surface is a strong interference source. We design some adaptive filters such as the least mean squares (LMS), recursive least squares (RLS) and Kalman filter to suppress this interference. The designed filters are applied to the experiment raw data, and the processing results show an obviously improved signal-to-interference ratio.