Marginal Distribution Multi-Target Bayes Filter With Assignment of Measurements

This work proposes a marginal distribution multi-target Bayes filter with assignment of measurements to track multiple targets in the presence of an unknown and variable number of targets, clutter, and missed detections. Mathematically, the association of the measurements with either a target or clutter may be established by maximizing the joint likelihood function of the measurement partition, which leads to a two-dimensional assignment problem. By the introduction of detecting label, a handling approach for missed detections is also developed and is applied to the proposed filter. This filter greatly reduces the number of hypothesized targets or Gaussian terms by selecting the predicted probability density of a target or one of its multiple updated probability densities as its state distribution at each time step. Experimental results indicate that the proposed filter requires a less computational load than the existing filters and performs better than the efficient implementations of the δ-generalized labeled multi-Bernoulli filter for multi-target tracking at low and moderate clutter densities.