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ACCURATE LOW SAMPLE RATE TRACKING OF HIGHLY MANEUVERING TARGETS USING H-TO…

Award Information

Agency:
Department of Defense
Branch:
Navy
Award ID:
18241
Program Year/Program:
1992 / SBIR
Agency Tracking Number:
18241
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
ALPHATECH, INC.
6 New England Executive Park Burlington, MA 01803
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Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 1992
Title: ACCURATE LOW SAMPLE RATE TRACKING OF HIGHLY MANEUVERING TARGETS USING H-TO INFINITY- NONLINEAR FILTERS
Agency / Branch: DOD / NAVY
Contract: N/A
Award Amount: $50,931.00
 

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.

Principal Investigator:

Dr Haralampos Tsaknakis
6172733388

Business Contact:

Small Business Information at Submission:

Alphatech, Inc.
50 Mall Road Burlington, MA 01803

EIN/Tax ID:
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No