Erkan Baser

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The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter removes the bias in the expected cardinality observed in the multi-target multi-Bernoulli (MeMBer) data update step. In this paper, a filter that offers a new statistical framework for the MeMBer data update step is proposed. Unlike the CBMeMBer filter, the proposed filter removes the(More)
We propose a novel auxiliary particle probability hypothesis density (AP-PHD) filter that elegantly combines the standard AP-filter with the particle PHD filter. The selection of particles in the proposed AP-PHD filter is based on maximizing the accuracy of the cardinality estimate. Moreover, the resampling is done on each auxiliary variable cluster(More)
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