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.

[1]  K. G. Murty.  An Algorithm for Ranking All the Assignment in Order of Increasing Cost , 1968 .

[2]  Ba-Ngu Vo,et al.  On performance evaluation of multi-object filters , 2008, 2008 11th International Conference on Information Fusion.

[3]  Ba-Ngu Vo,et al.  A Consistent Metric for Performance Evaluation of Multi-Object Filters , 2008, IEEE Transactions on Signal Processing.

[4]  Ba-Ngu Vo,et al.  Labeled Random Finite Sets and Multi-Object Conjugate Priors , 2013, IEEE Transactions on Signal Processing.

[5]  R. Mahler,et al.  PHD filters of higher order in target number , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[6]  R. Mahler.  PHD filters of higher order in target number , 2007 .

[7]  David Eppstein,et al.  Finding the k Shortest Paths , 1999, SIAM J. Comput..

[8]  Ba-Ngu Vo,et al.  The para-normal Bayes multi-target filter and the spooky effect , 2012, 2012 15th International Conference on Information Fusion.

[9]  Ronald Mahler.  Divergence detectors for multitarget tracking algorithms , 2013, Defense, Security, and Sensing.

[10]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[11]  R. Mahler.  Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[12]  Ronald P. S. Mahler,et al.  “Statistics 102” for Multisource-Multitarget Detection and Tracking , 2013, IEEE Journal of Selected Topics in Signal Processing.

[13]  Ba-Ngu Vo,et al.  A random finite set conjugate prior and application to multi-target tracking , 2011, 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[14]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[15]  Katta G. Murty,et al.  Letter to the Editor - An Algorithm for Ranking all the Assignments in Order of Increasing Cost , 1968, Oper. Res..