A multiple extended target filter based on random matrix is proposed to track the kinematic states and shape information of multiple targets in the presence of the clutter measurements. The proposed filter employs a joint partitioning algorithm
which combines the DBSCAN(density based spatial clustering of applications with noise)and the prediction partitioning algorithm
to partition the measurement set. Then
the JPDA(joint probabilistic data association)
which is a soft association algorithm
is applied to description of the association between measurement clusters and extended targets. Finally
the method of random matrix is employed to estimate the kinematic states and shape information of extended targets. Simulation results which compare the joint partitioning algorithm with DBSCAN partitioning show that the filter by using joint partitioning algorithm could achieve much better tracking performance than that by using DBSCAN partitioning when there are spatially close extended targets. Moreover
simulation results in comparison with the ET-GMPHD filter show that the proposed multiple extended target filter has higher tracking accuracy
higher detection probability
and lower false alarm probability.
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references
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