A NN type multi-sensor tracking and identification algorithm

A multiple dissimilar sensor multiple target tracking and identification algorithm in the spirit of the nearest neighbour (NN) class of assignment algorithms is proposed. The novelty of this work lies in the exploitation of information type Kalman filters and a coordinated approach to the treatment of both kinematic and non-kinematic properties.<<ETX>>

[1]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  M. Farooq,et al.  Robotic heterogeneous multi-sensor fusion with spatial and temporal alignment , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[3]  Lawrence A. Klein,et al.  Sensor and Data Fusion Concepts and Applications , 1993 .

[4]  James Llinas,et al.  Multisensor Data Fusion , 1990 .

[5]  Y. Bar-Shalom.  Tracking and data association , 1988 .

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

[7]  T. R. Rice,et al.  On the generalized nearest neighbor filter , 1990, [1990] Proceedings. The Twenty-Second Southeastern Symposium on System Theory.

[8]  Samuel S. Blackman,et al.  Multiple-Target Tracking with Radar Applications , 1986 .

[9]  Hirokazu Ihara,et al.  A track correlation algorithm for multi-sensor integration , 1987 .

[10]  D. L. Hall,et al.  Mathematical Techniques in Multisensor Data Fusion , 1992 .