Digital Signal Processing


On Point Processes in Multitarget Tracking

Authors: Ronald Mahler

The finite-set statistics (FISST) approach to multitarget tracking---random finite sets (RFS's), belief-mass functions, and set derivatives---was introduced in the mid-1990s. Its current extended form---probability generating functionals (p.g.fl.'s) and functional derivatives---dates from 2001. In 2008, an "elementary" alternative to FISST was proposed, based on "finite point processes" rather than RFS's. This was accompanied by single-sensor and multisensor versions of a claimed generalization of the PHD filter, the "multitarget intensity filter" or "iFilter." Then in 2013 in the Journal of Advances in Information Fusion (JAIF) and elsewhere, the same author went on to claim that the FISST p.g.fl./functional derivative approach is actually "due to" (a "corollary" of) a 50-year-old pure-mathematics paper by Moyal; and described a "point process" p.g.fl./functional derivative approach to multitarget tracking supposedly based on it. In this paper it is shown that: (1) non-RFS point processes are a phenomenologically erroneous foundation for multitarget tracking; (2) nearly every equation, concept, discussion, derivation, and methodology in the JAIF paper originally appeared in FISST publications, without being so attributed; (3) FISST cannot possibly be "due to Moyal"; and (4) the "point process" approach described in JAIF differs from FISST only in regard to terminology and notation, and thus in this sense appears to be an obscured, phenomenologically erroneous, and improperly attributed copy of FISST. The paper concludes with the following question: Given the above, do the peer-review standards of the Journal of Advances in Information Fusion rise to the level expected of any credible scientific journal? It is also shown that the derivations of the single-sensor and multisensor iFilter appear to have had major errors, as did a subsequent recasting of the multisensor iFilter as a "traffic mapping filter."

Comments: 18 Pages.

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[v1] 2018-03-22 20:47:27

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