ACCURATE LOW SAMPLE RATE TRACKING OF HIGHLY MANEUVERING TARGETS USING H-TO INFINITY- NONLINEAR FILTERS

Award Information
Agency:
Department of Defense
Amount:
$50,931.00
Program:
SBIR
Contract:
N/A
Solitcitation Year:
N/A
Solicitation Number:
N/A
Branch:
Navy
Award Year:
1992
Phase:
Phase I
Agency Tracking Number:
18241
Solicitation Topic Code:
N/A
Small Business Information
Alphatech, Inc.
50 Mall Road, Burlington, MA, 01803
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
N/A
Principal Investigator
 Dr Haralampos Tsaknakis
 (617) 273-3388
Business Contact
Phone: () -
Research Institution
N/A
Abstract
This exploratory development proposal addresses a class of novel algorithms specifically designed for tracking and engaging maneuvering air, naval or ground targets. The key technical innovation is the development of linear and nonlinear target tracking algorithms using the recent results in H(Infinity) estimation methodology to modify the classical Kalman filter and extended Kalman filter algorithms; these H(Infinity) based tracking algorithms actively hedge against "worst-case" target maneuvers and therefore ensure more robust tracking accuracy performance than do classical algorithms. Of particular interest is the version of these H(Infinity) tracking algorithms in which frequent (low data rate) radar measurements of the target(s) are being made, and the constrained target kinematic differential equations (linear or nonlinear) are used for target state prediction between successive measurements Robust prediction of the maneuvering target motion can also be incorporated into the algorithm, because H(Infinity) algorithms take advantage of "directional" information which is influenced by the desired prediction time. Phase I will demonstrate the performance improvement of these new tracking algorithms and the performance tradeoffs associated with reducing the data rate and the degree of maneuver acceleration. Phase II will integrate the phase I algorithms with other adaptive features, and demonstrate their performance tradeoffs using both simulated and, perhaps, actual data.

* information listed above is at the time of submission.

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