Ambiguous data association and entangled attribute estimation

David J. Trawick; Philip C. Du Toit; Randy C. Paffenroth; Gregory J. Norgard

doi:10.1117/12.920457

15 May 2012 Ambiguous data association and entangled attribute estimation

David J. Trawick, Philip C. Du Toit, Randy C. Paffenroth, Gregory J. Norgard

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Proceedings Volume 8393, Signal and Data Processing of Small Targets 2012; 83930H (2012) https://doi.org/10.1117/12.920457
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

This paper presents an approach to attribute estimation incorporating data association ambiguity. In modern tracking systems, time pressures often leave all but the most likely data association alternatives unexplored, possibly producing track inaccuracies. Numerica's Bayesian Network Tracking Database, a key part of its Tracker Adjunct Processor, captures and manages the data association ambiguity for further analysis and possible ambiguity reduction/resolution using subsequent data. Attributes are non-kinematic discrete sample space sensor data. They may be as distinctive as aircraft ID, or as broad as friend or foe. Attribute data may provide improvements to data association by a process known as Attribute Aided Tracking (AAT). Indeed, certain uniquely identifying attributes (e.g. aircraft ID), when continually reported, can be used to define data association (tracks are the collections of observations with the same ID). However, attribute data arriving infrequently, combined with erroneous choices from ambiguous data associations, can produce incorrect attribute and kinematic state estimation. Ambiguous data associations define the tracks that are entangled with each other. Attribute data observed on an entangled track then modify the attribute estimates on all tracks entangled with it. For example, if a red track and a blue track pass through a region of data association ambiguity, these tracks become entangled. Later red observations on one entangled track make the other track more blue, and reduce the data association ambiguity. Methods for this analysis have been derived and implemented for efficient forward filtering and forensic analysis.

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David J. Trawick, Philip C. Du Toit, Randy C. Paffenroth, and Gregory J. Norgard "Ambiguous data association and entangled attribute estimation", Proc. SPIE 8393, Signal and Data Processing of Small Targets 2012, 83930H (15 May 2012); https://doi.org/10.1117/12.920457

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KEYWORDS

Kinematics

Sensors

Data analysis

Data modeling

Computing systems

Databases

Electronic filtering

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