A Unified Bayesian Approach to Nonlinearity in Multitarget Tracking

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$149,999.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
FA8750-12-C-0054
Award Id:
n/a
Agency Tracking Number:
F112-042-2026
Solicitation Year:
2011
Solicitation Topic Code:
AF112-042
Solicitation Number:
2011.2
Small Business Information
500 West Cummings Park - Ste 3000, Woburn, MA, -
Hubzone Owned:
N
Minority Owned:
Y
Woman Owned:
N
Duns:
859244204
Principal Investigator:
Adel El-Fallah
Group Leader, Tracking and Fusion
(781) 933-5355
adel@ssci.com
Business Contact:
Jay Miselis
Corporate Controller
(781) 933-5355
contracts@ssci.com
Research Institute:
Stub




Abstract
ABSTRACT: Nonlinearity in target tracking poses a far more difficult challenge than is usually understood. Besides nonlinear sensor and target-motion models, one must also address"nonstandard"sources of nonlinearity: known or unknown sensor fields of view; known or unknown clutter; target appearance and disappearance, etc. Furthermore, multitarget tracking (MTT) algorithms are inherently nonlinear. Any systematic MTT analysis must consider all of these forms of nonlinearity. Scientific Systems Company, Inc. proposes a theoretically foundational approach to multitarget nonlinearity analysis. First, we will devise computationally tractable nonlinearity figures of merit for MTT, using theoretically rigorous methods. Second, we will exploit the fact that the multitarget recursive Bayes filter inherently accounts for the above forms of nonlinearity. Specifically, we will develop approximations of the multitarget Bayes filter that comprehensively incorporate the above nonlinear models. We will also investigate new approximate filters that can operate in unknown backgrounds, including unknown clutter and unknown detection profiles. We will implement these algorithms using Gaussian mixture and/or particle-based techniques, and test their sensitivities to the various forms of nonlinearity just mentioned. The project team includes Dr. Ronald Mahler of Lockheed Martin. Lockheed Martin will provide both technical and commercialization support in the application of multitarget tracking technologies. BENEFIT: Unified nonlinearity-resistant multitarget tracking (MTT) algorithms are of major interest to all branches of the military. Commercial application includes law enforcement, industrial and homeland security, air traffic control, and weather radar applications.

* information listed above is at the time of submission.

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