Three-dimensional optimum nonlinear filter for detecting distorted ladar images in disjoint background noise

We propose an optimum nonlinear filter to detect the target's 3D coordinates within the input 3D scene using LADAR data. The 2D encoded LADAR range image is converted into 3D binary profile, and then the 3D optimum nonlinear filtering technique is used to detect the 3D coordinates of targets including the target distance from the sensor. The 3D optimum nonlinear filter is designed to detect distorted targets, i.e., out-of-plane and in-plane rotations and scale, and to be noise robust. The nonlinear filter is derived to minimize the mean of the output energy due to the disjoint background noise and additive noise and output energy due to the input scene, while maintaining a fixed output peak for the members of the true class target training set. The system is tested using real LADAR imagery in the presence of background clutter. The correlation outputs of LADAR images show dominant peaks at the target 3D coordinates.