17 May 2006 Multi-environment NLF tracking assessment testbed (MENTAT): an update
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Abstract
In applications in which even the best EKFs and MHTs may perform poorly, the single-target and multi-target Bayes nonlinear filters become potentially important. In recent years, new implementation techniques such as sequential Monte Carlo (a.k.a. particle-system) have emerged that, when hosted on ever more inexpensive, smaller, and powerful computers, make these filters potentially computationally tractable for real-time applications. A methodology for preliminary test and evaluation (PT&E) of the relative strengths and weaknesses of these algorithms is becoming increasingly necessary. The purpose of PT&E is to (1) assess the broad strengths and weaknesses of various algorithms or algorithm types; (2) justify further algorithm development; and (3) provide guidance as to which algorithms are potentially useful for which applications. At last year's conference we described our plans for the development of a PT&E tool, MENTAT. In this paper we report on current progress. Our implementation is MATLAB-based, and harnesses the GUI-building capabilities of the well-known MATLAB package, SIMULINK.
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Ronald Mahler, Joseph Spinks, Michael Ekhaus, Lingji Chen, "Multi-environment NLF tracking assessment testbed (MENTAT): an update", Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62350Q (17 May 2006); doi: 10.1117/12.667090; https://doi.org/10.1117/12.667090
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