Adaptive Information Matrix Filtering fusion with nonlinear classifier

An adaptive filtering approach is present for fusing the tracks of multi-sensor surveillance systems. The approach is an algorithm of hierarchical estimation fusion which consists of several local nodes and a global node. A linear Kalman filter is employed by each local node to produce the track estimate of the same target. The outputs of all local nodes are transmitted to the global node. In this node, an adaptive filter, which consists of the dual-band Information Matrix Filter (IMF) and a nonlinear classifier, is utilized to combine the local estimates to generate a global estimate appropriately. The feature vectors are yielded by dual-band IMF. The classifier, which is designed by using the radial basis function network in Gaussian form, is used to divide the feature space into regions that correspond to decide the output of either high-level-band IMF or low-level-band IMF against the uncertainties of target dynamics. The proposed filter has better tracking performance than each individual IMF. Simulation results are included to demonstrate the effectiveness of proposed filter.

[1]  Thiagalingam Kirubarajan,et al.  Performance limits of track-to-track fusion versus centralized estimation: theory and application [sensor fusion] , 2003 .

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

[3]  Li-Wei Fong,et al.  Multi‐sensor track‐to‐track fusion via linear minimum variance sense estimators , 2008 .

[4]  Yunmin Zhu,et al.  The optimality for the distributed Kalman filtering fusion with feedback , 2001, Autom..

[5]  B. Friedland.  Optimum Steady-State Position and Velocity Estimation Using Noisy Sampled Position Data , 1973, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Tian Zhi,et al.  Performance Evaluation of Track Fusion with Information , 2002 .

[7]  X. R. Li,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[8]  Li-Wei Fong.  Integrated Track-to-Track Fusion with Modified Probabilistic Neural Network , 2006, 2006 CIE International Conference on Radar.

[9]  Li-Wei Fong.  An adaptive filter for multi-sensor track fusion , 2008, 2008 9th International Conference on Signal Processing.

[10]  Li-Wei Fong,et al.  Distributed data fusion algorithms for tracking a maneuvering target , 2007, 2007 10th International Conference on Information Fusion.

[11]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[12]  J. A. Roecker,et al.  Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion , 1988 .

[13]  K.V. Ramachandra,et al.  Optimum Steady State Position, Velocity, and Acceleration Estimation Using Noisy Sampled Position Data , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[15]  L. Varshney.  Radar Principles , 2005 .

[16]  Byron Edde.  Radar: Principles, Technology, Applications , 1992 .