The multisensor PHD filter: I. General solution via multitarget calculus

Ronald Mahler

doi:10.1117/12.818024

11 May 2009 The multisensor PHD filter: I. General solution via multitarget calculus

Ronald Mahler

Proceedings Volume 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII; 73360E (2009) https://doi.org/10.1117/12.818024
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

The theoretical foundation for the probability hypothesis density (PHD) filter is the FISST multitarget differential and integral calculus. The "core" PHD filter presumes a single sensor. Theoretically rigorous formulas for the multisensor PHD filter can be derived using the FISST calculus, but are computationally intractable. A less theoretically desirable solution-the iterated-corrector approximation-must be used instead. Recently, it has been argued that an "elementary" methodology, the "Poisson-intensity approach," renders FISST obsolete. It has further been claimed that the iterated-corrector approximation is suspect, and in its place an allegedly superior "general multisensor intensity filter" has been proposed. In this and a companion paper I demonstrate that it is these claims which are erroneous. This paper introduces formulas for the actual "general multisensor intensity filter." In the companion paper I demonstrate that the "general multisensor intensity filter" will perform badly in even the easiest multitarget tracking problems; and argue that this suggests that the "Poisson-intensity approach" is inherently faulty.

Citation Download Citation

Ronald Mahler "The multisensor PHD filter: I. General solution via multitarget calculus", Proc. SPIE 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII, 73360E (11 May 2009); https://doi.org/10.1117/12.818024

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