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This paper describes a set of methods that make it possible to estimate the position of a feature inside a three-dimensional (3D) space by starting from a sequence of two-dimensional (2D) acoustic images of the seafloor acquired with a sonar system. Typical sonar imaging systems are able to generate just 2D images, and the acquisition of 3D information involves sharp increases in complexity and costs. The front-scan sonar proposed in this paper is a new equipment devoted to acquiring a 2D image of the seafloor to sail over, and allows one to collect a sequence of images showing a specific feature during the approach of the ship. This fact seems to make it possible to recover the 3D position of a feature by comparing the feature positions along the sequence of images acquired from different (known) ship positions. This opportunity is investigated in the paper, where it is shown that encouraging results have been obtained by a processing chain composed of some blocks devoted to low-level processing, feature extraction and analysis, a Kalman filter for robust feature tracking, and some ad hoc equations for depth estimation and averaging. A statistical error analysis demonstrated the great potential of the proposed system also if some inaccuracies affect the sonar measures and the knowledge of the ship position. This was also confirmed by several tests performed on both simulated and real sequences, obtaining satisfactory results on both the feature tracking and, above all, the estimation of the 3D position.