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Motion parameter estimation has been one of the most important objectives for target tracking. And holographic systems have significant advantage over conventional motion detection devices because of the capability of detecting the range information. Motion estimation is usually performed in the image domain after the image reconstruction process. Typically, motion estimation operates in the discrete mode given a finite number of selected point features with correspondence. These constraints and requirements reduce the feasibility of motion estimation for real-time target tracking. Recently, a statistical motion estimation method has been developed for holographic acoustical systems. This methods utilizes the mean and covariance matrix to identify the translation vector and rotation matrix operation associated with the target motion. This method is capable of performing motion estimation in both space and spatial-frequency domains and it does not require point feature selection and correspondence identification, and therefore, it signi-ficantly enhances the potential for real-time tracking. In this paper, we first introduce the development and formulation of this statistical motion estimation method. To separate the translation component of the motion from the rotation operator, we perform the motion estimation in the spatial-frequency domain using the power spectra of the received wave-field sequences for the identification of the rotation operator and the phase variation for the estimation of the translation vector. This method can be also directly applied to navigation and position identification for radar systems, and displacement estimation in tomographic reconstruction.
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