Abstract
Adaptively modeling the target birth intensity while maintaining the filtering efficiency is a challenging issue in multi-target tracking (MTT). Generally, the target birth probability is predefined as a constant and only the target birth density is considered in existing adaptive birth models, resulting in deteriorated target tracking accuracy, especially in the target appearing cases. In addition, the existing adaptive birth models also give rise to a decline in operation efficiency on account of the extra birth modeling calculations. To properly adapt the real variation of the number of newborn targets and improve the multi-target tracking performance, a novel fast sequential Monte Carlo (SMC) adaptive target birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is proposed in this paper. Through adaptively conducting the target birth probability in a pre-processing step, which incorporates the information of current measurements to correct the pre-setting of the target birth probability, the proposed filter can truly adapt target birth cases and achieve better tracking accuracy. Moreover, the implementation efficiency can be improved significantly by employing a measurement noise-based threshold in the likelihood calculations of the multi-target updating. Simulation results verify the effectiveness of the proposed filter.
Highlights
IntroductionThe core objective in multi-target tracking (MTT) is to estimate the states of multiple moving targets, based on obtained data with spontaneous appearance and disappearance of different targets
In the sequential Monte Carlo (SMC) multi-Bernoulli filters, the clustering algorithm to extract the states of targets can be avoided, while it is required in the SMC probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters
In Reference [11], an adaptive target birth model was applied in the PHD and CPHD filters by grouping the targets into newborn and surviving sets, whose target birth density was generated through the measurements of the previous time step
Summary
The core objective in multi-target tracking (MTT) is to estimate the states of multiple moving targets, based on obtained data with spontaneous appearance and disappearance of different targets. In Reference [11], an adaptive target birth model was applied in the PHD and CPHD filters by grouping the targets into newborn and surviving sets, whose target birth density was generated through the measurements of the previous time step. We propose an improved multi-target Bayesian filter based on the CBMeMBer framework which can adapt unknown target birth cases including both density and probability. The target birth density is modeled by the measurements received at the previous moment, which is similar to the method in Reference [13], while the target birth probability is calculated by a pre-processing step using the current received measurements This CBMeMBer filter with the new adaptive target birth intensity eliminates the requirement of prior birth information, and avoids the coarse assumption of the number of newborn targets.
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