Joint Target Tracking and Classification with Heterogeneous Sensors
AbstractTarget classification and target tracking are conventionally treated as two separate problems. The target tracking is usually performed using data from kinematic sensors like radar while target classification is performed using data from attribute/feature sensors like ESM. There are few consideration of the link between the target state and the target class. However, the methods of integrating target tracking and classification into a single framework does make sense. In this paper we present a joint target tracking and classification(JTC) approach. The approach presented here integrates the target class information into the data association process of target tracking. The target classification information results from the Bayesian classifier based on the confusion matrix which accounts for the uncertainty of target classification process. Performance comparisons with and without the use of the class information in target tracking are illustrated. The simulation results suggest that the proposed JTC algorithm provide a more accurate solution to the target tracking problem using heterogeneous sensors. Ill. 5, bibl. 10 (in English; abstracts in English and Lithuanian).
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