Target classification algorithm based on feature aided tracking

Ronghui Zhan; Jun Zhang

doi:10.1117/12.2010883

14 March 2013 Target classification algorithm based on feature aided tracking

Ronghui Zhan, Jun Zhang

Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 87682G (2013) https://doi.org/10.1117/12.2010883
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore

Abstract

An effective target classification algorithm based on feature aided tracking (FAT) is proposed, using the length of target (target extent) as the classification information. To implement the algorithm, the Rao-Blackwellised unscented Kalman filter (RBUKF) is used to jointly estimate the kinematic state and target extent; meanwhile the joint probability data association (JPDA) algorithm is exploited to implement multi-target data association aided by target down-range extent. Simulation results under different condition show the presented algorithm is both accurate and robust, and it is suitable for the application of near spaced targets tracking and classification under the environment of dense clutters.

Citation Download Citation

Ronghui Zhan and Jun Zhang "Target classification algorithm based on feature aided tracking", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87682G (14 March 2013); https://doi.org/10.1117/12.2010883

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