Problems with information fusion under extreme multiple-counting conditions

V. Nicholas Panei; Jonathan S. Taylor

doi:10.1117/12.2521141

7 May 2019 Problems with information fusion under extreme multiple-counting conditions

Proceedings Volume 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII; 110180A (2019) https://doi.org/10.1117/12.2521141
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

This document concerns three information fusion methods: Information Matrix Fusion (IMF), Covariance Intersection (CI), and Sampling Covariance Intersection (SCI). These methods are compared for performance under extreme multiple-counting conditions, that is, when an estimate is improperly fused to a track multiple times as if the estimate was repeatedly found by independent measurements. This situation can possibly occur in networked fusion systems where data pedigree is less than properly maintained, especially when an information relay is implemented to handle diminished communication environments. Extreme multiple-counting behavior in particular is examined for the purposes of this document. This research demonstrates that the normally preferable methods, IMF and SCI, are prone to falsely optimistic covariance values in such situations. All three fusion methods result in the state estimate approaching the estimate being repeatedly fused; the more conservative CI method also results in the covariance approaching that of the repeated estimate. We obtain these results through inference from the governing equations and examination of behavior under Monte Carlo simulations.

Citation Download Citation

V. Nicholas Panei and Jonathan S. Taylor "Problems with information fusion under extreme multiple-counting conditions", Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 110180A (7 May 2019); https://doi.org/10.1117/12.2521141

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KEYWORDS

Information fusion

Data fusion

Monte Carlo methods

Sensors

Detection and tracking algorithms

Telecommunications

Computing systems

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