MENTAT: a benchmark evaluation testbed for nonlinear filtering
Ronald Mahler, John R. Hoffman, Lingji Chen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Many nonlinear filtering (NLF) algorithms have been proposed in recent years for application to single- and multi-target detection and tracking. A methodology for preliminary test and evaluatin (PT&E) of these algorithms is becoming increasingly necessary. Under U.S. Army Research Office funding, Scientific Systems Co. Inc. and Lockheed Martin are developing a Multi-Environment NLF Tracking Assessment Testbed (MENTAT) to address this need. Once completed, MENTAT is to provide a "hierarchical" series of preliminary test and evaluation (PT&E) Monte Carlo simulated environments (including benchmark problems) of increasing difficulty and realism. The simplest MENTAT environment will consist of simple 2D scenarios with simple Gaussian-noise backgrounds and simple target maneuvers. The most complicated environments will involve: (1) increasingly more realistic simulated low-SNR backgrounds; (2) increasing motion and sensor nonlinearity; (3) increasingly higher state dimensionality; (4) increasing numbers of targets; and so on.