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Proceedings Paper

MENTAT: a benchmark evaluation testbed for nonlinear filtering
Author(s): Ronald Mahler; John Hoffman; Lingji Chen
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Paper Abstract

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.

Paper Details

Date Published: 25 May 2005
PDF: 12 pages
Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); doi: 10.1117/12.604714
Show Author Affiliations
Ronald Mahler, Lockheed Martin MS2 Tactical Systems (United States)
John Hoffman, Lockheed Martin MS2 Tactical Systems (United States)
Lingji Chen, Scientific Systems Co., Inc. (United States)


Published in SPIE Proceedings Vol. 5809:
Signal Processing, Sensor Fusion, and Target Recognition XIV
Ivan Kadar, Editor(s)

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