Tracking of targets with state dependent measurement errors using recursive BLUE filters

In this paper, optimal best linear unbiased estimation (BLUE) filters are derived for cases where measurement errors depend on the state of the target. The standard Kalman filter fails to provide optimal estimates in these cases. Previously applied measurement models are reformulated in order to apply BLUE filters, and two new measurement models with state dependent biases are proposed. It is shown how the higher order unscented transform may be used to approximate the terms in the BLUE filter when they are not available analytically. The BLUE filters are shown by Monte Carlo simulations to have better performance than other suboptimal filters.

[1]  Oddvar Hallingstad,et al.  Tracking and fusion of surveillance radar images of extended targets , 2007, 2007 10th International Conference on Information Fusion.

[2]  X. R. Li,et al.  A Survey of Maneuvering Target Tracking—Part III: Measurement Models , 2001 .

[3]  Oddvar Hallingstad,et al.  The error statistics of surveillance radar position measurements , 2007 .

[4]  Jeffrey K. Uhlmann,et al.  A consistent, debiased method for converting between polar and Cartesian coordinate systems , 1997 .

[5]  W D Blair,et al.  Use of Target-Oriented Process Noise in Tracking Maneuvering Targets , 1992 .

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

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

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

[9]  Edward W. Kamen,et al.  Modeling and fusion of radar and imaging sensor data for target tracking , 1996 .

[10]  T. Papaioannou.  Information, Measures of , 2006 .

[11]  Y. Bar-Shalom,et al.  Tracking of Divers using a Probabilistic Data Association Filter with a Bubble Model , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Jesús García,et al.  Model-based trajectory reconstruction using IMM smoothing and motion pattern identification , 2007, 2007 10th International Conference on Information Fusion.

[13]  Y.D. Shirman,et al.  Computer simulation of aerial target radar scattering recognition, detection, and tracking , 2003, IEEE Aerospace and Electronic Systems Magazine.