Estimation of linear target-layer trajectories using cluttered point cloud data

Abstract

The problem of estimating a target-layer trajectory, modeled by a straight line, in 2D point clouds that contain target locations and overwhelming clutter is studied. These point clouds are generated by an image-based pre-processing tool, termed ATR, operating on SONAR image data that results in: (1) point locations and (2) an ATR score: a measure of the “target-likeness” for each point. The model of choice assumes that the observed point cloud is a superposition of two spatial processes: (1) a 1D Poisson process along the target-layer line, corrupted by 2D Gaussian noise, denoting target locations and (2) a 2D Poisson process denoting clutter. It is further assumed that the target-likeness measure follows known probability distributions for both target locations and clutter. The line is parameterized by distance from the origin and the angle with respect to a horizontal axis, and the likelihood of these parameters for observed data is derived. Using a maximum-likelihood approach, a gradient-based estimate for line parameters and other nuisance parameters is developed. A formal procedure that tests for the presence of a target-layer trajectory in the point cloud data is additionally developed. The success of this method in both simulated and real datasets collected by NSWC PCD is demonstrated.