Analysis of data and model accuracy requirements for target classification using ladar data

Considerable attention has recently been focused on laser radar (ladar) systems for surveillance applications, because of the richness inherent in the three-dimensional data they collect. Several research groups have looked into the ability to exploit ladar data in automatic or assisted target recognition systems. Designers of practical ladar recognition systems must have answers to very fundamental questions related to the quality and quantity of data required, the fidelity of target models used by the algorithm, and the effects of incorporating prior knowledge. This paper presents implementation guidelines derived from a simulation-based analysis of many such factors that can affect classification accuracy, including measurement noise and corresponding noise model accuracy, number of measured points on target, target model accuracy, target pose error, prior information, and target occlusion. The study includes data from eight vehicles, including seven civilian automobiles of similar size and shape, chosen specifically to result in a "hard" classification problem. Data are simulated under three hypothetical scenarios: 1) a pole-mounted ladar system for monitoring traffic through an intersection; 2) a low-flying helicopter-born ladar system for law enforcement applications; and 3) a low-altitude unmanned aerial vehicle (UAV) for military surveillance applications.