Image navigation method using parametric systematic error correction
View Patent ↗A novel Parametric Systematic Error Correction (ParSEC) system is disclosed which provides improved system accuracy for image navigation and registration (INR). This system may be employed in any suitable imaging system and, more specifically, to all imaging systems that exhibit systematic distortion. The ParSEC system may be employed to any such system regardless of sensing type (remote or in situ) or imaging media (photon or charged particle) and is further applicable to corrected imaging of any celestial body currently detectable to remove distortion and systematic error from the imaging system employed. The ParSEC system of the instant invention comprises a software algorithm that generates at least about 12 correction coefficients for each of the INR system measurements such as stars, visible landmarks, infrared (IR) landmarks and earth edges. An iterative estimation algorithm such as, for example, least squares or Kalman filters may be employed to determine the at least about 12 correction coefficients from each set of measurement residuals. The improved image products provide more accurate weather forecasting such as wind velocity and temperature.
1. A method of providing improved image navigation and registration (INR) for an imaging system that exhibits systematic distortion comprising:
collecting imagery data comprising measurements of star and landmark locations using the imaging system;
processing, via at least one processor, actual and predicted star and landmark location data using Parametric Systematic Error Correction (ParSEC) comprising about 12 correction coefficients to iteratively estimate the about 12 ParSEC coefficients for each location measurement; and
correcting, via at least one processor, location measurements using the estimated ParSEC coefficients.
2. The method as defined in claim 1 , wherein said about 12 coefficients are determined by an iterative estimation algorithm comprising measured residuals associated with each measurement.
3. The method as defined in claim 2 further comprising applying the about 12 coefficients to correct the measured residuals.
4. The method as defined in claim 1 , wherein INR measurements include stars, visible landmarks, infrared (HO landmarks and earth edges.
5. The method as defined in claim 2 , wherein said iterative estimation algorithm comprises least squares to determine the about 12 correction coefficients.
6. The method as defined in claim 2 , wherein said iterative estimation algorithm comprises Kalman Filter to determine the about 12 correction coefficients.
7. The method as defined in claim 2 , wherein said measured residuals comprise the difference between the actual measurement of East-West and North-South coordinates from imagery data and predicted East-West and North-South coordinates based on orbit and attitude solution.
8. The method as defined in claim 1 , wherein said applying the about 12 coefficients improves orbit and attitude determination.
9. The method as defined in claim 1 , wherein said applying the about 12 coefficients improves remapping of raw image pixels to better match an actual coastline to a fixed coastline grid.
10. The method as defined in claim 1 , wherein said applying the about 12 coefficients improves image products to facilitate High Rate Information Transmission (HRIT) and Low Rate Information Transmission (LRIT).
11. The method as defined in claim 1 , wherein said applying the about 12 coefficients improves image products resulting in improved weather forecasting, including wind velocity and temperature.
12. A method of providing improved image registration and navigation (INR) for imaging systems that exhibit systematic distortion comprising;
collecting actual measurements selected from a group including star, visible landmarks, infrared (IR) landmark and earth edges measurements from an imaging camera positioned on a moving satellite over about a one day period of time;
computing, via at least one processor, measurement residuals as the difference between actual and predicted locations;
fitting measurement residuals employing a Parametric Systematic Error Correction (ParSEC) coefficient determination algorithm in at least one processor to determine a best estimate of the about 12 ParSEC coefficients for each type of measurement;
employing the about 12 ParSEC coefficients to correct locations of each of an actual star, visible landmark, infrared (HO landmark or earth edge measurement; and
repeating the fitting measurement residuals step until the about 12 ParSEC coefficients stabilize.
13. The method as defined in claim 12 , wherein visible landmark coefficients correct locations of raw visible imagery pixels to better match a fixed coastline overlay grid.
14. The method as defined in claim 12 , wherein IR landmark coefficients correct locations of raw IR imagery pixels to better match a fixed coastline overlay grid.
15. The method as defined in claim 14 , wherein more accurate geo-location in image products such as High Rate Information Transmission HRIT) and Low Rate Information Transmission (LRIT) is obtained before transmittal to users.
16. The method as defined in claim 12 , wherein the about 12 ParSEC coefficients are applied in reverse to modify future expected star observation locations in order to better predict a star's location in lieu of adverse systematic errors affecting an imaging system.
17. The method as defined in claim 12 , further comprising:
employing the actual measurements to determine an orbit and attitude solution using an orbit and attitude determination system (OADS); and
computing predicted locations for the measurements employing an orbit and attitude (O&A) solution from the OADS.
18. The method as defined in claim 1 , wherein said correcting measured imagery data comprises:
processing measured imagery data comprising INR measurements derived from the imaging system using Parametric Systematic Error Correction (ParSEC) comprising about 12 correction coefficients;
determining the about 12 correction coefficients; and
applying the about 12 correction coefficients to each measurement to correct for systemic distortion exhibited by the imaging system.