Gaussian Sum Filters for Space Surveillance: Theory and Simulations

While standard Kalman-based filters, Gaussian assumptions, and covariance-weighted metrics are very effective in data-rich tracking environments, their use in the data-sparse environment of space surveillance ismore limited. To properly characterize non-Gaussian density functions arising in the problem of long-term propagation of state uncertainties, a Gaussian sum filter adapted to the two-body problem in space surveillance is proposed and demonstrated to achieve uncertainty consistency. The proposed filter is made efficient by using only a onedimensional Gaussian sum in equinoctial orbital elements, thereby avoiding the expensive representation of a full six-dimensional mixture and hence the “curse of dimensionality.” Additionally, an alternate set of equinoctial elements is proposed and is shown to provide enhanced uncertainty consistently over the traditional element set. Simulation studies illustrate the improvements in theGaussian sumapproach over the traditional unscentedKalman filter and the impact of correct uncertainty representation in the problems of data association (correlation) and anomaly (maneuver) detection.

[1]  Dimitrios Hatzinakos,et al.  An adaptive Gaussian sum algorithm for radar tracking , 1997, Proceedings of ICC'97 - International Conference on Communications.

[2]  A. Genz,et al.  Fully symmetric interpolatory rules for multiple integrals over infinite regions with Gaussian weight , 1996 .

[3]  Keith D. Kastella,et al.  Foundations and Applications of Sensor Management , 2010 .

[4]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[5]  Chris Sabol,et al.  Linearized Orbit Covariance Generation and Propagation Analysis via Simple Monte Carlo Simulations (Preprint) , 2010 .

[6]  Qing Liu,et al.  A note on Gauss—Hermite quadrature , 1994 .

[7]  Aubrey B. Poore,et al.  Adaptive Gaussian Sum Filters for Space Surveillance Tracking , 2012 .

[8]  Aubrey B. Poore,et al.  Covariance consistency for track initiation using Gauss-Hermite quadrature , 2010, Defense + Commercial Sensing.

[9]  Aubrey B. Poore,et al.  Multidimensional assignment formulation of data association problems arising from multitarget and multisensor tracking , 1994, Comput. Optim. Appl..

[10]  R. Park,et al.  Nonlinear Mapping of Gaussian Statistics: Theory and Applications to Spacecraft Trajectory Design , 2006 .

[11]  H. Sorenson,et al.  Nonlinear Bayesian estimation using Gaussian sum approximations , 1972 .

[12]  Aubrey B. Poore,et al.  Nonlinear least-squares estimation for sensor and navigation biases , 2006, SPIE Defense + Commercial Sensing.

[13]  Aubrey B. Poore,et al.  Adaptive Gaussian Sum Filters for Space Surveillance , 2011, IEEE Transactions on Automatic Control.

[14]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[15]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[16]  Oliver Montenbruck,et al.  Satellite Orbits: Models, Methods and Applications , 2000 .

[17]  Paul J. Cefola,et al.  Entropy-Based Space Object Data Association Using an Adaptive Gaussian Sum Filter , 2010 .

[18]  A. Jazwinski Stochastic Processes and Filtering Theory , 1970 .

[19]  Moriba Jah,et al.  TP-2009-1023 TP-2009-1023 ORBIT DETERMINATION PERFORMANCE IMPROVEMENTS FOR HIGH AREA-TO-MASS RATIO SPACE OBJECT TRACKING USING AN ADAPTIVE GAUSSIAN MIXTURES EXTIMATION ALGORITHM : PREPRINT , 2009 .

[20]  T. Singh,et al.  Uncertainty Propagation for Nonlinear Dynamic Systems Using Gaussian Mixture Models , 2008 .

[21]  R. Broucke,et al.  On the equinoctial orbit elements , 1972 .

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