COLORED NOISE SIMULATION AND CHARACTERIZATION, AND EFFECTS ON ATTITUDE ACCURACY

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$49,921.00
Award Year:
1991
Program:
SBIR
Phase:
Phase I
Contract:
n/a
Award Id:
17041
Agency Tracking Number:
17041
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
6100 Chevy Chase Dr #200, Laurel, MD, 20707
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
() -
Business Contact:
() -
Research Institute:
n/a
Abstract
STANDARD TECHNIQUES FOR THE ESTIMATION OF ATTITUDE-DETERMINATION ACCURACY ASSUME THAT THE INPUT SENSOR DATA HAS UNCORRELATED, WHITE NOISE, AND THAT SIMULATORS GENERALLY PROVIDE WHITE NOISE. HOWEVER, REAL SENSOR NOISE IS GENERALLY OBSERVED TO HAVE MORE DOMINANT LOWER-FREQUENCY COMPONENTS. THEREFORE, THIS PROJECT WILL DEVELOP SOFTWARE TO SIMULATE COLORED NOISE EFFICIENTLY, TEST THE NOISE CHARACTERISTICS, AND ESTIMATE THE IMPACT OF THE NOISE CHARACTERISTICS ON THE PREDICTED ATTITUDE-DETERMINATION ACCURACY. THIS PROJECT WILL ALSO EXPLORE EFFICIENT-NOISE CHARACTERIZATION AS A MEANS OF AUTOMATICALLY IDENTIFYING SUBTLE ANOMALIES IN REAL DATA. THIS WORK IS NEW AND WILL ADD AN IMPORTANT PERSPECTIVE IN THE UNDERSTANDING OF OBTAINABLE ATTITUDE ACCURACLES WITH REALISTIC DATA. IT WILL ALSO PROVIDE TOOLS FOR REALISTIC DATA SIMULATION, FLIGHT-DATA CHARACTERIZATION, AND ANOMALY IDENTIFICATION. THESE TOOLS WILL BE DEVELOPED ON A PC. STANDARD TECHNIQUES FOR THE ESTIMATION OF ATTITUDE-DETERMINATION ACCURACY ASSUME THAT THE INPUT SENSOR DATA HAS UNCORRELATED, WHITE NOISE, AND THAT SIMULATORS GENERALLY PROVIDE WHITE NOISE. HOWEVER, REAL SENSOR NOISE IS GENERALLY OBSERVED TO HAVE MORE DOMINANT LOWER-FREQUENCY COMPONENTS. THEREFORE, THIS PROJECT WILL DEVELOP SOFTWARE TO SIMULATE COLORED NOISE EFFICIENTLY, TEST THE NOISE CHARACTERISTICS, AND ESTIMATE THE IMPACT OF THE NOISE CHARACTERISTICS ON THE PREDICTED ATTITUDE-DETERMINATION ACCURACY. THIS PROJECT WILL ALSO EXPLORE EFFICIENT-NOISE CHARACTERIZATION AS A MEANS OF AUTOMATICALLY IDENTIFYING SUBTLE ANOMALIES IN REAL DATA. THIS WORK IS NEW AND WILL ADD AN IMPORTANT PERSPECTIVE IN THE UNDERSTANDING OF OBTAINABLE ATTITUDE ACCURACLES WITH REALISTIC DATA. IT WILL ALSO PROVIDE TOOLS FOR REALISTIC DATA SIMULATION, FLIGHT-DATA CHARACTERIZATION, AND ANOMALY IDENTIFICATION. THESE TOOLS WILL BE DEVELOPED ON A PC.

* information listed above is at the time of submission.

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