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Improved Estimation Approaches for High-Accuracy Satellite Detection, Tracking, Identification and Characterization

Description:

OBJECTIVE: Develop improved algorithms capable of fusing and exploiting existing and/or planned space surveillance data sources to improve ability to detect, track, identify, and characterize man-made space objects. DESCRIPTION: Detecting, tracking, identifying, characterizing, and cataloguing of space objects is a difficult Air Force mission that involves maintaining a catalog of over 20K+ objects. This is currently accomplished by exploiting a disparate network of sensors having a variety of missions other than space surveillance. No single object is observed persistently given the scarcity of sensors and priority-based tasking. The sparcity of data requires improved methods of data association, namely the ability to confidently ascribe measurements to unique objects. Given the increasing number of launches and proliferation of space debris, the task of identifying and discriminating space objects is becoming more challenging. Most space objects have unknown size, shape, material properties, and rotational dynamics, further complicating the issue. The collected data set is derived from both radar (range) and optical (line-of-sight or bearings-only) sensors. Along with these data, object radar cross-section as well as radiometry in one or more wavelengths is available yet not fully exploited for its fused information content. In essence, the problem can be generalized to one of multi-sensor/multi-target tracking with probability of detection less than one and often times no a priori information. The space object dynamic and model parameter state of both continuous and discrete random variables is hidden/unknown and only observed indirectly via the reduction of non-linear, corrupted, noisy, and biased measurements. Moreover, the current representation of space object ambiguity is artificially constrained to that of Gaussian distributed errors. Thus, the proposed work needs to achieve a number of goals: (1) improve the ability to fuse and exploit existing data sources to detect new and/or lost space objects; (2) to identify newly detected objects and allow measurements to be properly associated with them with measureable and quantifiable ambiguity in the presence of false detections (i.e., clutter); (3) to produce precise and accurate estimates of the object's motion parameters (e.g., position/velocity/orientation/rates, orbital elements, etc.) amenable to long-term prediction of future motion; (4) to allow detection of changes to these parameters and the identification of trends and patterns in the data suitable for long-term scheduling of future observations; (5) identify and develop computationally tractable methods to enable the development and maintenance of a large catalog of space objects; (6) identify, develop, and implement methods that allow for realistic inference and prediction of space object state uncertainty. This topic seeks innovative algorithms that maximally exploit the physical interaction of the space environment with space objects to achieve these goals. Priority is given to techniques with sound, rigorous mathematical foundations that treat the problem holistically (i.e., processes that preserve the probability density function (PDF), can handle space object birth/death, clutter, probability of detection, probability of object existence, non-Gaussianity, non-linearity, etc., in a unified framework, e.g., finite set statistics (FISST)). Techniques must have traceability and scalability to the end-application described above. PHASE I: Evaluate and develop candidate algorithms that will improve the estimation of the satellite location and trends suitable for long-term scheduling of future observations; establish measurement requirements that are compatible with current space object surveillance sensors, select feasible scenarios; evaluate algorithms against measurement requirements vs. estimation improvements. PHASE II: Apply the results of Phase I to the implementation of algorithms into prototype software; Evaluate algorithm performance against requirements in computer simulations. Apply algorithms to actual sensor data to determine performance. PHASE III: Military applications include Space Situational Awareness; results should also have application in target detection, tracking, identification, & characterization in other domains. Commercial applications include flight traffic control & traffic management systems as well as scientific applications. REFERENCES: 1. Kelecy, T., Jah, M., (2010). Initial Strategies to Recover and Predict High Area-to-Mass Ratio (HAMR) GEO Space Object Trajectories with No A Priori Information. Journal of the International Academy of Astronautics: Acta Astronautica, Accepted (05/09/11). 2. DeMars, K., Jah, M., Schumacher, P., Jr., (2010). The Use of Angle and Angle Rate Data for Deep-Space Orbit Determination and Track Association, AAS Paper 10-153, 20th AAS/AIAA Space Flight Mechanics Meeting, San Diego, CA, February 14-17. Accepted IEEE Transactions on Aerospace and Electronic Systems, Jun 2011. 3. DeMars, K., Bishop, R., Jah, M., (2011). A Splitting Gaussian Mixture Method for the Propagation of Uncertainty in Orbital Mechanics, Advances in the Astronautical Sciences, Vol. 140, pp. 1419-1438, Univelt, San Diego. AAS Paper 11-201. 4. Wetterer, C.J., Jah, M.K., (2009), Using the Unscented Kalman Filter for Attitude Determination from Light Curves, Journal of Guidance, Control, and Dynamics Vol.32, No.5, pp. 1648-1651. 5. Kelecy, T., Jah, M., (2008). Maneuver Detection and Orbit Determination Of A Low Earth Orbiting Satellite Executing Low Thrust Finite Burn Maneuvers. Journal of the International Academy of Astronautics: Acta Astronautica, In Press (08/24/09). 6. Goodman, I. R.,Mahler, R. P. S., and Nguyen, H. T.,Mathematics of Data Fusion, Kluwer Academic Publishers, 1997. 7. Mahler, R. P. S., Statistical Multisource-Multitarget Information Fusion, Artec House, 2007. 8 Vo, B. and Ma, W.,"The Gaussian Mixture Probability Hypothesis Density Filter,"IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol. 54, No. 11, November 2006. 9. Mahler, R. P. S.,"Multitarget Markov Motion Models,"SPIE Conference on Signal Processing, Sensor Fusion, and Target Recognition, Vol. 3720, 1999, pp. 4756. 10. DeMars, K. J., Bishop, R. H., and Jah,M. K.,"A Splitting Gaussian Mixture Method for the Propagation of Uncertainty in Orbital Mechanics,"21st AAS/AIAA Space Flight Mechanics Meeting, February 2011. 11. DeMars, K. J., Bishop, R. H., and Jah, M. K.,"Propagation of Uncertainty in the Presence of Attitude-Dependent Solar Radiation Pressure Effects."AAS/AIAA Astrodynamics Specialist Conference, July/August 2011. 12. Hussein, I., DeMars, K. J., Fruh, C., Erwin, R. S., and Jah,M. K.,"An AEGIS-FISST Integrated Detection and Tracking Approach to Space Situational Awareness,"International Conference on Information Fusion, July 2012.
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