TARGET RECOGNITION TRACKING AND RESPONSE WITH NEURAL NETWORK KALMAN-BUCY FILTERS

Award Information
Agency:
Department of Defense
Branch:
Navy
Amount:
$160,000.00
Award Year:
1990
Program:
SBIR
Phase:
Phase II
Contract:
N/A
Agency Tracking Number:
9910
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Martingale Research Corp.
100 Allentown Pkwy - Ste 211, Allen, TX, 75002
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
N/A
Principal Investigator
 Dr Robert L Dawes
 (214) 422-4570
Business Contact
Phone: () -
Research Institution
N/A
Abstract
THE PROPOSED RESEARCH WILL INCORPORATE THE PARAMETRIC AVALANCHE NEURAL NETWORK ARCHITECTURE INTO A MAXIMUM LIKELIHOOD DETECTION AND TRACKING ALGORITHM FOR USE IN AUTONOMOUS WEAPON SYSTEMS. THE PARAMETRIC AVALANCHE WILL PERFORM O(1) SEARCH IN ITS STORED PATTERN FEATURES TO IDENTIFY THE TARGET AND ITS MOTION EQUATIONS, AFTER WHICH IT WILL TRACK THE TARGET. A SECOND PARAMETRIC AVALANCH WILL USE THE STATE ESTIMATES FROM THE FIRST, TOGETHER WITH A REFERENCE TRAJECTORY SUPPLIED PERHAPS BY AN EXPERT SYSTEM, TO GENERATE CONTROL SIGNALS FOR GUIDANCE OF THE AUTONOMOUS WEAPON SYSTEM. THE PARAMETRIC AVALANCHE IS A RECURRENT TWO-LAYER NEURAL NETWORK WHICH EMPLOYS A PROPRIETARY TECHNIQUE FOR UNSUPERVISED LEARNING OF AN INTERNAL MODEL OF OBSERVED DYNAMICAL SYSTEMS AND SUBSEQUENTLY EMPLOYING THAT MODEL IN A CONTINUOUS BAYESIAN ESTIMATOR (GENERALIZING THE KALMAN-BUCY FILTER). THE ARCHITECTURE EMPLOYS A NOVELTY FILTER TO WHITEN THE PREDICTABLES IN THE SCENE, AND IT NEEDS NO A-PRIOR KNOWLEDGE OF STRUCTURE IN THE SCENE, SO THAT IT CAN IN PRINCIPLE INTEGRATE SENSOR INPUTS FROM VARIOUS SOURCES. TRACKING TAKES PLACE SUBLIMINALLY UNTIL CONTINUOUS LIKELIHOOD REINFORCEMENT TRIGGERS DETECTION.

* information listed above is at the time of submission.

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