1. 10/31/01 - 1 Simultaneous Estimation of Aircraft and Target Position With a Control Point Professor Dominick Andrisani Purdue University, School of Aeronautics and Astronautics 1282 Grissom Hall, West Lafayette, IN 47907-1282 andrisan@ecn.purdue.edu 765-494-5135 Presented at the The Motion Imagery Geolocation Workshop SAIC Signal Hill Complex, October 31, 2001 http://bridge.ecn.purdue.edu/~uav

2. 10/31/01 - 2 Purpose: To determine the benefits of simultaneously estimating aircraft position and unknown target position when there is also a control point (target of known location). This involves coupling the aircraft navigator (INS, GPS, or integrated INS/GPS) and the image-based target position estimator and image data from the unknown target and known control point.

3. 10/31/01 - 3 Model and Parameters to Drive Simulation Aircraft Motion Aircraft Model Trajectory Input Time Input Turbulence Input Errors GPS Satellite Constellation Processing Mode Antennas Number, Location Errors INS Position, Attitude, Rates Filter Aircraft Position & Attitude Estimate and Uncertainty Transformation to Sensor Position, Attitude, and Uncertainty Errors Sensor Parameters Image Acquisition Parameters Site Model Imaging System Target Coordinates Uncertainty, CE90 Graphic Animation Multi-Image Intersection Synthetic Image Generation Errors Target Tracking Do these simultaneously rather then serially. Image target and Control point.

4. 10/31/01 - 4 Hypothesis: Given a combined estimator of aircraft position and target position capable of imaging on a unknown target and a known control point. If a control point enters the field of view of the image system, the accuracy of simultaneous estimation of aircraft position and unknown target position will be significantly improved.

5. 10/31/01 - 5 Technical Approach Use a linear low-order simulation of a simplified linear aircraft model, Use a simple linear estimator to gain insight into the problem with a minimum of complexity. A control point of known location will enter the field of view of the image processor only during the time from 80-100 seconds.

6. 10/31/01 - 6 0 Linear Simulation: Fly-Over Trajectory Unknown Target always visible Initial aircraft position time=0 sec Final aircraft position time=200 sec -10,00010,000 Range Meas., R (ft) Position (ft) Image Coord. Meas. x (micron) Position Meas X aircraft (ft) Focal Plane (f=150 mm) Camera always looks down. 20,000 Nominal speed=100 ft/sec Data every.1 sec., i.e., every 10 ft Control point Known location Visible only from time=80-100 seconds.

7. 10/31/01 - 7 Nominal Measurement noise assumed in the simulation  Aircraft position = 1 feet  Image coordinate = 7.5 microns  Range = 1 feet

8. 10/31/01 - 8 State Space Model State equation x(j+1)=  (j,j-1)x(j)+v(j)+w(j) Measurement equation z(j)=h(x(j))+u(j) x(o)=x 0 (Gaussian initial condition) where v(j) is a known input w(j) is Gaussian white process noise u(j) is Gaussian white measurement noise

9. 10/31/01 - 9 The Kalman Filter State Estimator Initialize Predict one step Measurement update

10. 10/31/01 - 10

11. 10/31/01 - 11 Residuals of the Kalman Filter No measurement here

12. 10/31/01 - 12 Estimated state -Actual state Major impact of control point here Major impact of control point here

13. 10/31/01 - 13 Expanded time scale for Estimated state -Actual state Major impact of control point here

14. 10/31/01 - 14 Estimated state and actual state time histories

15. 10/31/01 - 15 Estimated state -Actual state

16. 10/31/01 - 16 Expanded time scale for Estimated state -Actual state Major impact of control point here

17. 10/31/01 - 17 Estimated state -Actual state No impact of control point Little impact of control point here

18. 10/31/01 - 18 Expanded time scale for Estimated state -Actual state Littler impact of control point here

19. 10/31/01 - 19 Estimated state -Actual state No impact of control point here

20. 10/31/01 - 20 Expanded time scale for Estimated state -Actual state No impact of control point here

21. 10/31/01 - 21 “New Black Box Navigator” with camera #1 on Target #1. Image-based target Locator using camera #2 on target #2. Improved aircraft position Improved target position Two Useful Scenarios Aircraft and target #1 and #2 data Integrated navigator and image processor using one camera to simultaneously or sequentially track two targets. Aircraft and target #1 data Improved target position Target #2 data

22. 10/31/01 - 22 Conclusions 1. When the measurement noise on aircraft position is large (sigmaXL>>1), the sighting of a known control point significantly improves the aircraft position accuracy AND the unknown target position accuracy. This suggests a that flying over control points is tactically useful! 2. In my talk at the last workshop at Purdue we concluded that a dramatic improvement of aircraft position estimation suggests a new type of navigator should be developed. This navigator would integrate INS, GPS, and image processor looking at known or unknown objects on the ground. One or two cameras might be used.

23. 10/31/01 - 23 Related Literature 1. B.H. Hafskjold, B. Jalving, P.E. Hagen, K. Grade, Integrated Camera-Based Navigation, Journal of Navigation, Volume 53, No. 2, pp. 237-245. 2. Daniel J. Biezad, Integrated Navigation and Guidance Systems, AIAA Education Series, 1999. 3. D.H. Titterton and J.L. Weston, Strapdown Inertial Navigation Technology, Peter Peregrinus, Ltd., 1997. 4. A. Lawrence, Modern Inertial Technology, Springer, 1998. 5. B. Stietler and H. Winter, Gyroscopic Instruments and Their Application to Flight Testing, AGARDograph No. 160, Vol. 15,1982. 6. A.K. Brown, High Accuracy Targeting Using a GPS-Aided Inertial Measurement Unit, ION 54th Annual Meeting, June 1998, Denver, CO.