Divergence detectors for the δ-generalized labeled multi-Bernoulli filter

In single-target tracking, divergence detectors like the normalized innovation squared (NIS) are used to detect if the assumed motion or measurement models deviate too much from the actual behavior of the tracked target or the sensor. A generalization of the divergence detectors to random finite set based multi-object tracking algorithms is possible and results in the multi-target generalized NIS (MGNIS). In this contribution the MGNIS for the δ-generalized labeled multi-Bernoulli filter is derived. Further, an approximate multi-target NIS (AMNIS) is proposed which facilitates easier interpretation of the results. The MGNIS and the AMNIS are compared to the well-known optimal subpattern assignment (OSPA) metric using simulated data with different clutter rates.