SBIR-STTR Award

Thousand object tracking with neural nets
Award last edited on: 2/12/04

Sponsored Program
SBIR
Awarding Agency
DOD : MDA
Total Award Amount
$544,838
Award Phase
2
Solicitation Topic Code
MDA88-010
Principal Investigator
Victor Eliashberg

Company Information

Cardinal Research LLC (AKA: Memistor Corporation~Cardinal Research LLC)

860 Lanthrop Drive
Stanford, CA 94305
   (650) 857-9151
   N/A
   N/A
Location: Single
Congr. District: 18
County: Santa Clara

Phase I

Contract Number: 30494
Start Date: 00/00/00    Completed: 00/00/00
Phase I year
1988
Phase I Amount
$68,838
New computational means need to be developed for the practical solution of real-time tracking of a thousand or more space-borne objects at one time. It is assumed that, during an engagement, data would arrive from a number of sensors which may be located in a number of places, observing objects in space with and without overlapping coverage. The sensor data would be fed to computer equipment dedicated to the tracking task, and the output would be the time histories of detected objects giving their track numbers and positions versus time. Conventional computers cannot perform the tracking function at real-time rates with a load of a thousand or more objects to track. Many of the functions can be done with neural networks that can be implemented in a highly parallel structure. System architecture is being developed to implement this tracking approach. Effects of sensor noise and sensor signal quantization on probabilities of false track initiation and losing a track are being determined. A preliminary hardware design is being undertaken for the tracking approach. When successful, this research would lead to the design of a practical, reliable, economical system for real-time simultaneous tracking of a thousand plus space-borne objects. Solution of this problem also would address the nation's tracking problem in the air traffic control area.

Phase II

Contract Number: DASG60-89-C-0021
Start Date: 3/23/90    Completed: 12/31/91
Phase II year
1989
Phase II Amount
$476,000
A neural network parallel processing system is being designed for simultaneously tracking more than a thousand space-borne targets. The system captures input from a variety of sensors including radar, infrared, and laser radar. The parallel neural processing of the vast input data permits real time tracking, giving a time history of each detected object and identifying each track number with target position and time. Conventional computers cannot process the volume of data on a thousand or more targets simultaneously for real time tracking. In addition to space vehicle tracking, the developed neural network could be highly beneficial if applied to the nation's air traffic control system.