Abstract
The original multi-target multi-Bernoulli (MeMBer) filter for multi-target tracking (MTT) is shown analytically to have a significant bias in its cardinality estimation. A novel cardinality balance multi-Bernoulli (CBMeMBer) filter reduces the cardinality bias by calculating the exact cardinality of the posterior probability generating functional (PGFl) without the second assumption of the original MeMBer filter. However, the CBMeMBer filter can only have a good performance under a high detection probability, and retains the first assumption of the MeMBer filter, which requires measurements that are well separated in the surveillance region. An improved MeMBer filter proposed by Baser et al. alleviates the cardinality bias by modifying the legacy tracks. Although the cardinality is balanced, the improved algorithm employs a low clutter density approximation. In this paper, we propose a novel structure for a multi-Bernoulli filter without a cardinality bias, termed as a novel multi-Bernoulli (N-MB) filter. We remove the approximations employed in the original MeMBer filter, and consequently, the N-MB filter performs well in a high clutter intensity and low signal-to-noise environment. Numerical simulations highlight the improved tracking performance of the proposed filter.
Highlights
Considering the multi-target tracking (MTT) environments, the unknown number of targets changes with time because of the presence of target deaths and births
Based on the finite set statistics (FISST) theory, Mahler proposed a rigorous formulation of random finite set (RFS)-type filters
The multi-target multi-Bernoulli (MeMBer) filter parameterizes the multi-target distribution that models each potential target as a single Bernoulli RFS [3], which is characterized by the probability of existence and the probability density function
Summary
Considering the multi-target tracking (MTT) environments, the unknown number of targets changes with time because of the presence of target deaths and births. The MeMBer filter parameterizes the multi-target distribution that models each potential target as a single Bernoulli RFS [3], which is characterized by the probability of existence and the probability density function (pdf). The prior multi-target distribution is the same with the posterior multi-target distribution in the PMBM filter [17,18] Another Bayes-closed filter is derived based on the well-known generalized labeled multi-Bernoulli distribution (GLMB) [14,15,16], along with a relatively efficient version, the well-known δ-GLMB filter, which propagates the whole data association history together with track sets; it is computationally expensive. I-MeMBer filters retain the first approximation of the MeMBer filter [3], which requires measurements that are well-separated in surveillance region They cannot perform well in a surveillance region with proximity targets and/or high clutter density environments.
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