Probabilistic objective functions for sensor management

Ronald P. S. Mahler; Tim R. Zajic

doi:10.1117/12.543530

9 August 2004 Probabilistic objective functions for sensor management

Ronald P. S. Mahler, Tim R. Zajic

Proceedings Volume 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII; (2004) https://doi.org/10.1117/12.543530
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

This paper continues the investigation of a foundational and yet potentially practical basis for control-theoretic sensor management, using a comprehensive, intuitive, system-level Bayesian paradigm based on finite-set statistics (FISST). In this paper we report our most recent progress, focusing on multistep look-ahead -- i.e., allocation of sensor resources throughout an entire future time-window. We determine future sensor states in the time-window using a "probabilistically natural" sensor management objective function, the posterior expected number of targets (PENT). This objective function is constructed using a new "maxi-PIMS" optimization strategy that hedges against unknowable future observation-collections. PENT is used in conjuction with approximate multitarget filters: the probability hypothesis density (PHD) filter or the multi-hypothesis correlator (MHC) filter.

Citation Download Citation

Ronald P. S. Mahler and Tim R. Zajic "Probabilistic objective functions for sensor management", Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); https://doi.org/10.1117/12.543530

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