Target identification with Bayesian networks in a multiple hypothesis tracking system

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Abstract
A multisensor fusion algorithm that integrates model-based target identification (ID) and multiple-hypothesis tracking (MHT) is described. The algorithm augments the target state representation to include ID information and to manipulate the new target state. The major innovation of the algorithm is using Bayesian networks to modify the hypothesis-evaluation process by taking into account target ID. This research was conducted as part of a larger effort to design a decision-theoretic sensor management system. In the system, two types of sensors, electronically scanned radar (ESA) and IR search and track (IRST) are assumed to be available. The ESA radar is modeled to have search and update capabilities as well as three radar identification modes: ultra-high-resolution radar (UHRR), radar signal modulation (RSM), and radar electronic support mode (RESM). A Bayesian network is used to model the detection and observation processes for these ID techniques and compute the association likelihoods between measurements and tracks. © 1997 Society of Photo-Optical Instrumentation Engineers. Key words: sensor fusion; Bayesian networks; multiple hypothesis tracking systems.
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