Sensor Geodetic Registration for Tactical Ballistic Missile (TBM) Composite Tracking and Discrimination Capability for Army System of Systems (ASoS) I

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$578,619.00
Award Year:
2010
Program:
SBIR
Phase:
Phase II
Contract:
W9113M-10-C-0095
Award Id:
92100
Agency Tracking Number:
A091-012-0163
Solicitation Year:
n/a
Solicitation Topic Code:
ARMY 09-012
Solicitation Number:
n/a
Small Business Information
340 N. Westlake Blvd., Suite 118, Thousand Oaks, CA, 91362
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
068577175
Principal Investigator:
Phillip Dennis
Chief Scientist
(805) 558-6058
Phillip.Dennis@isrweb.com
Business Contact:
William Cook
President
(805) 573-5234
William.Cook@isrweb.com
Research Institute:
n/a
Abstract
Critical to the success of distributed sensor fusion for IAMD is the ability of each network sensor to correctly associate sensor measurement and attribute data to tracked objects within the air/missile picture. To achieve this, multi-sensor fusion requires sensor registration techniques that ensure network sensor are sufficiently synchronized and spatially aligned. The goal of sensor registration is to reduce the range of uncertainty inherent in a given single radar by considering the "cross correlations" that exist between the data on common tracks observed by network sensors. Our approach to sensor registration is to provide geodetically aligned sensor data within a component based distributed architecture using methods that act on geodetic calibration tracks and other fiducial data points common to the distributed sensors to produce location and orientation bias estimates at each sensor. The distributed architecture allows each sensor to compute local biases and apply the computed "offsets" prior to distribution of track data to the tracking network. Sensor registration using simultaneous observation of selected common tracks and fiducial points among distributed sensors has the potential to compute a local sensor registration solution by averaging out the uncorrelated random bias errors that will be present within the ensemble of networked sensors.

* information listed above is at the time of submission.

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