Image navigation and registration accuracy improvement using parametric systematic error correction

Abstract

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 imaging systems that exhibit systematic distortion comprising:
 employing a Parametric Systematic Error Correction (ParSEC) system, said ParSEC system comprising at least about 12 correction coefficients for each of the INR systems measured.   
   
   
       2 . The method as defined in  claim 1 , wherein said at least about 12 coefficients being determined by an iterative estimation algorithm from each set of measured residuals. 
   
   
       3 . The method as defined in  claim 2  further comprising applying the at least about 12 coefficients for each set of measurements to correct the measured residuals. 
   
   
       4 . The method as defined in  claim 1 , wherein the INR systems measured include stars, visible landmarks, IR landmarks and earth edges. 
   
   
       5 . The method as defined in  claim 1 , wherein said iterative estimation algorithm comprises least squares to determine the at least about 12 correction coefficients. 
   
   
       6 . The method as defined in  claim 1 , wherein said iterative estimation algorithm comprises Kalman Filter to determine the at least about 12 correction coefficients. 
   
   
       7 . The process as defined in  claim 1 , wherein said measurement 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 at least about 12 coefficients improves orbit and attitude determination process. 
   
   
       9 . The method as defined in  claim 1 , wherein said applying the at least about 12 coefficients improves remapping of raw image pixels to better match fixed coastline grid to actual coastline. 
   
   
       10 . The method as defined in  claim 1 , wherein said applying the at least about 12 coefficients improves image product 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 at least about 12 coefficients improves image product 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 from a group including star, visible landmarks, IR landmark and earth edges measurements from an imaging camera positioned on a moving satellite over at least about a one day period of time;   employing the actual measurements to determine an orbit and attitude solution using an orbit and attitude determination system (OADS);   computing predicted locations for the measurements employing an orbit and attitude (O&A) solution from the OADS;   computing the measurement residuals as the difference between actual and predicted locations;   fitting measurement residuals employing the ParSEC coefficient determination algorithm in order to determine the best estimate of the at least about 12 ParSEC coefficients for each type of measurement;   employing the at least about 12 ParSEC coefficients to correct East-West and North-South location of each of an actual star, visible landmark, IR landmark or earth edges measurements; and   repeating the fitting measurement residual step until the at least about 12 ParSEC coefficients stabilize.   
   
   
       13 . The method as defined in  claim 12 , wherein the visible landmark at least about 12 ParSEC coefficients correct location of raw visible imagery pixels to better match fixed coastline overlay grid and achieve more accurate geo-location in image products such as HRIT/LRIT before transmittal to users. 
   
   
       14 . The method as defined in  claim 12 , wherein the IR landmark at least about 12 ParSEC coefficients correct location of raw IR imagery pixels to better match fixed coastline overlay grid. 
   
   
       15 . The method as defined in  claim 14 , wherein more accurate geo-location in image products such as HRIT/LRIT is obtained before transmittal to users. 
   
   
       16 . The method as defined in  claim 12 , wherein the at least 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 the imager. 
   
   
       17 . A method of providing improved image navigation and registration (INR) for imaging systems that exhibit systematic distortion comprising:
 employing a Parametric Systematic Error Correction (ParSEC) system wherein coefficients for said system are determined by employing an nth order Taylor Series.   
   
   
       18 . The method as defined in  claim 1 , wherein the nth order Taylor Series equals 1 and either the E or N variable is used. 
   
   
       19 . The method as defined in  claim 1 , wherein the nth order Taylor Series equals 1 and E and N are used as variables. 
   
   
       20 . The method as defined in  claim 1 , wherein the nth order Taylor Series equals 2 and either the E or N is used as variables. 
   
   
       21 . The method as defined in  claim 1 , wherein the nth order Taylor Series equals 2 and E and N are employed as variables. 
   
   
       22 . The method as defined in  claim 1 , wherein the nth order Taylor Series equals 3 and either of E or N is employed as variables. 
   
   
       23 . The method as defined in  claim 1 , wherein the nth order Taylor Series equals 3 and E and N are employed as variables. 
   
   
       24 . An imaging system having improved image navigation and registration (INR), comprising:
 an imaging system that exhibits systematic distortion;   a camera pointing apparatus that adjusts pointing direction of the camera;   an onboard computer coupled to the camera and the camera pointing apparatus for controlling the pointing direction of the camera; and   one or more sensors selected from a group including a star tracker, a central body sensor and a gyro, wherein said computer employs a Parametric Systematic Error Correction (ParSEC) system.   
   
   
       25 . The system as defined in  claim 24 , wherein said imaging system comprises a camera onboard a spacecraft in orbit. 
   
   
       26 . The imaging system as defined in  claim 25 , wherein said ParSEC system comprises at least about 12 correction coefficients for each of the INR systems measured. 
   
   
       27 . The imaging system as defined in  claim 26 , wherein the INR systems measured comprise stars, visible landmarks, IR landmarks and earth edges. 
   
   
       28 . The imaging system as defined in  claim 27 , wherein said at least about 12 coefficients of the ParSEC system are determined by an iterative estimation algorithm from each set of measured residuals. 
   
   
       29 . The imaging system as defined in  claim 28 , wherein said at least about 12 coefficients are applied for each set of measurements to correct measured residuals. 
   
   
       30 . The imaging system as defined in  claim 29 , wherein said iterative estimation algorithm comprises least squares to determine the at least about 12 correction coefficients. 
   
   
       31 . The imaging system as defined in  claim 30 , wherein said measured residuals comprise difference between 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. 
   
   
       32 . A system for providing improved image navigation and registration (INR) for imaging systems that exhibit systematic distortion, comprising:
 one or more sensors for collecting actual measurements relating to star, visible landmark, IR landmark and earth edge measurements from an imaging camera positioned on a moving satellite over at least about a one day period of time;   an onboard computer which employs the actual measurements to determine an orbit and attitude (O&A) solution using an orbit and attitude determination system (OADS);   said computer predicts locations for the measurements employing an O&A solution from the OADS;   said computer measures residuals as the difference between the actual and predicted locations and applies a ParSEC coefficient determination algorithm in order to determine best estimate of at least about 12 ParSEC coefficients for each type of measurement, said computer employs the at least about 12 ParSEC coefficients to correct East-West and North-South location of each of an actual star, visual landmark, IR landmark or earth edge measurements; and   said computer again applies the measured residuals until the at least about 12 ParSEC coefficients stabilize.   
   
   
       33 . An imaging system having improved image navigation and registration (INR), comprising:
 an imaging system that exhibits systematic distortion;   a camera pointing apparatus that adjusts pointing direction of the camera;   an onboard computer coupled to the camera and the camera pointing apparatus for controlling the pointing direction of the camera; and   one or more sensors selected from a group including a star tracker, a central body sensor and a gyro, wherein said computer employs a Parametric Systematic Error Correction (ParSEC) system wherein coefficients for said system are determined by employing an nth order Taylor Series.   
   
   
       34 . The imaging system as defined in  claim 33 , wherein the nth order Taylor Series equals 1 and either the E or N is used as variables. 
   
   
       35 . The imaging system as defined in  claim 33 , wherein the nth order Taylor Series equals 1 and E and N are used as variables. 
   
   
       36 . The imaging system as defined in  claim 33 , wherein the nth order Taylor Series equals 2 and either the E or N is used as variables. 
   
   
       37 . The imaging system as defined in  claim 33 , wherein the nth order Taylor Series equals 2 and E and N are used as variables. 
   
   
       38 . The imaging system as defined in  claim 33 , wherein the nth order Taylor Series equals 3 and either of E or N is used as variables. 
   
   
       39 . The imaging system as defined in  claim 33 , wherein the nth order Taylor Series equals 3 and E and N are used as variables.