Comparison of filtering algorithms for ground target tracking using space-based GMTI radar

Space-based radar (SBR) systems have received a great deal of attention, since they can provide all-weather, day-night, and continuous world-wide surveillance and tracking of ground, air, and sea-surface targets. The ground moving target indicator (GMTI) mode is an important operating mode for such systems. GMTI radar measurements are the range, azimuth and range-rate, which are nonlinear functions of the target state. We consider the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) for the SBR GMTI nonlinear filtering problem and present a new track initiation algorithm. We compare the mean square errors (MSEs) and computational times using simulated data generated by Monte Carlo simulations. Although the cross-range errors are large, our results show that the MSEs of the filters are nearly the same. Our results show that the EKF performs the best for the scenario considered based on the MSE and computational time.

[1]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

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

[3]  Y. Bar-Shalom,et al.  Unbiased converted measurements for tracking , 1998 .

[4]  Petros G. Voulgaris,et al.  On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..

[5]  G. R. Curry.  A low-cost space-based radar system concept , 1996, Proceedings of the 1996 IEEE National Radar Conference.

[6]  Mark R. Morelande,et al.  Application of particle filters to single-target tracking problems , 2003, SPIE Optics + Photonics.

[7]  P. Rosen,et al.  Perspectives on Worldwide Spaceborne Radar Programs , 2007, 2007 IEEE Radar Conference.

[8]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[9]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[10]  Sanjeev Arulampalam,et al.  Comparison of nonlinear filtering algorithms in ground moving target indicator (GMTI) tracking , 2003, SPIE Optics + Photonics.

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

[12]  M.E. Davis,et al.  Space based radar technology challenges , 2005, 2005 IEEE Aerospace Conference.

[13]  Leopold J. Cantafio,et al.  Space-Based Radar Handbook , 1989 .

[14]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

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

[16]  A. M. Walton,et al.  Space Based Radar Tracking Filter , 1995 .

[17]  A. Doucet,et al.  A Tutorial on Particle Filtering and Smoothing: Fifteen years later , 2008 .

[18]  Robert R. Bitmead,et al.  Conditions for stability of the extended Kalman filter and their application to the frequency tracking problem , 1995, Math. Control. Signals Syst..

[19]  S. Unnikrishna Pillai.  Space Based Radar: Theory & Applications , 2008 .

[20]  M. Mallick,et al.  Differential Geometry Measures of Nonlinearity with Applications to Ground Target Tracking , 2004 .