Orbital Evasive Target Tracking and Sensor Management

In this chapter, we consider the sensor management problem for tracking space targets where the targets may apply evasive maneuvering strategy to avoid being tracked by the space borne observers. We first study the case of single target tracking by a single observer and formulate the pursuit–evasion game with complete information. Then we extend the tracking problem to a set of collaborative observers and each observer has to decide when to sense which target in order to achieve the desired estimation error covariance. A popularly used criterion for sensor management is to maximize the total information gain in the observer-to-target assignment. We compare the information based approach to the game theoretic criterion where the observers are assigned according to the best response of the terminal result in the pursuit–evasion game. Finally, we use realistic satellite orbits to simulate the space resource management for situation awareness. We adopted NASA’s General Mission Analysis Tool (GMAT) for space target tracking with multiple space borne observers. The results indicate that the game theoretic approach is more effective than the information based approach in handling intelligent target maneuvers.

[1]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[2]  D. D. Mueller,et al.  Fundamentals of Astrodynamics , 1971 .

[3]  Nguyen Duong,et al.  Orbit determination by range-only data. , 1973 .

[4]  V.L. Pisacane,et al.  Orbit Determination from Passive Range Observations , 1974, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Felix R. Hoots,et al.  General Perturbations Theories Derived from the 1965 Lane Drag Theory , 1979 .

[6]  J. Fowler,et al.  An extended Kalman filter in a dynamic spherical coordinate (DYSC) system for space based satellite tracking , 1985 .

[7]  Yaakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Applications and Advances , 1992 .

[8]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[9]  D. Vallado Fundamentals of Astrodynamics and Applications , 1997 .

[10]  Yaakov Bar-Shalom,et al.  Multitarget/Multisensor Tracking: Applications and Advances -- Volume III , 2000 .

[11]  H. V. Trees Detection, Estimation, And Modulation Theory , 2001 .

[12]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[13]  M. Kalandros Covariance control for multisensor systems , 2002 .

[14]  LI X.RONG,et al.  Best linear unbiased filtering with nonlinear measurements for target tracking , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[15]  V. Jilkov,et al.  Survey of maneuvering target tracking. Part V. Multiple-model methods , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Genshe Chen,et al.  A range rate based detection technique for tracking a maneuvering target , 2005, SPIE Optics + Photonics.

[17]  H. V. Trees,et al.  Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking , 2007 .

[18]  Alfred O. Hero,et al.  An Information-Based Approach to Sensor Management in Large Dynamic Networks , 2007, Proceedings of the IEEE.

[19]  D.S. Bernstein,et al.  Spacecraft tracking using sampled-data Kalman filters , 2008, IEEE Control Systems.

[20]  Erik Blasch,et al.  Comparison of several space target tracking filters , 2009, Defense + Commercial Sensing.

[21]  Jose B. Cruz,et al.  Awareness-based game-theoretic space resource management , 2009, Defense + Commercial Sensing.