Hybrid Kalman Filter and Neural Network for GPS-IMU Tracking Data

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$603,735.00
Award Year:
2011
Program:
SBIR
Phase:
Phase II
Contract:
FA9302-11-C-0003
Agency Tracking Number:
F083-265-1546
Solicitation Year:
2008
Solicitation Topic Code:
AF083-265
Solicitation Number:
2008.3
Small Business Information
Photonics Optics Tech
23733 Maple Leaf Ct., Valencia, CA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
957081222
Principal Investigator:
Tien-Hsin Chao
Vice President
(661) 878-5552
potincca@gmail.com
Business Contact:
Tien-Hsin Chao
Vice President
(661) 878-5552
potincca@gmail.com
Research Institution:
Stub




Abstract
ABSTRACT: The scope of the proposed Phase II task is to develop an intelligent MOSES system that is capable of autonomous updating parameter file of the KF within the MOSES. This will enable the dynamic updating and reduction of KF errors during the flight vehicle trajectory post-processing. The proposed work develop is based on the successful Phase I preliminary Kalman Filter Neural Network algorithms design and the trajectory reconstruction simulation study that have proved the soundness of the proposed approach using a trained NN to improve the KF performance. The Phase II R & D effort will include the development of 1) A Clustering Ensemble Approached based Neural Network that will generate input to the MOSES parameter files updating. Extensive experimental studies will be performed using the intelligent MOSES system with real flight trajectory data to demonstrate its performance capability. BENEFIT: Upon completion of the development of the advanced intelligent MOSES system that is capable of autonomous updating parameter file of the KF within the MOSES. This intelligent MOSES software will enable the dynamic updating and reduction of KF errors during the flight vehicle trajectory post-processing.The real-time update capability will benefit the MOSES operations in at least two ways: 1. Shorten the trajectory post-processing time using the SOA MOSES due to the elimination of the"human-in-the-loop"parameters tuning work. 2. Improve the trajectory reconstruction accuracy due to the in-time update of the KF parameter files.

* information listed above is at the time of submission.

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