Fast Online Prediction of Aircraft State Trajectories (FORECAST) System

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,999.00
Award Year:
2009
Program:
STTR
Phase:
Phase I
Contract:
N68335-09-C-0590
Award Id:
90260
Agency Tracking Number:
N09A-005-0479
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
500 West Cummings Park - Ste 3000, Woburn, MA, 01801
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
859244204
Principal Investigator:
JovanBoscovic
Group Leader, Auton. & Int. Control
(781) 933-5355
Jovan.Boskovic@ssci.com
Business Contact:
JayMiselis
Corporate Controller
(781) 933-5355
contracts@ssci.com
Research Institute:
MIT
Jonathan How
77 Massachusetts Avenue
Room 33-236
Cambridge, MA, 2139
(617) 253-3267
Nonprofit college or university
Abstract
The SSCI team proposes to develop and test the on-board Fast Online pREdiCtion of Aircraft State Trajectories (FORECAST) system, using minimum state information such as 3-D position of a threat aircraft, to generate predicted trajectories and reachable sets T seconds into the future. It will be based on a nonlinear constrained stochastic model of aircraft dynamics involving rapid maneuvering, advanced nonlinear filtering techniques, and the design of the predicted exclusion zone for the aircraft operating in the vicinity of the threat aircraft. The algorithms used to develop the FORECAST technology will include: multi-model nonlinear filtering using Interacting Multiple Models; Extended Kalman Filter; Fokker Planck Equation; and exclusion zone calculation using stochastic feedback version of the Rapidly-exploring Random Trees algorithm. In Phase I, we will test the FORECAST system on a simplified scenario simulation. The Option will include extensive testing on a higher-fidelity simulation. In Phase II, we will continue algorithm development, perform extensive simulations and flight testing at MIT''s RAVEN facility, and develop the FORECAST software toolbox. Our academic partner, Prof. Jonathan How of MIT, brings in a wealth of expertise and experience in the area of 4-D trajectory planning, autonomous UAV control, multi-agent collaboration, and advanced flight test facilities.

* information listed above is at the time of submission.

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