Publications

Version: 1.0 Approved

LDO-ID 2021-02-26-03

Title Multiple sensor fusion and consistency

Abstract

The paper investigates the problems relevant to the fusion of data provided by sensors used for detection and tracking. One of the most burdensome problems on any data fusion in these fields of application is the search for the appropriate correlation function between the estimation errors of the tracks from the various sensors. The absence of such a function can lead to excessively optimistic cross-covariance matrices between tracks with consequent inconsistencies and missed associations. The paper has the aim to provide an effective solution to this problem. The proposed technique resides to build consistent cross covariance matrices by means of a tracking system which uses as input the simultaneous associations of the tracks of the involved sensors. The method results simple and fast as well as precise. An adapted version of the well-known Joint Probabilistic Data Association algorithm is also proposed to manage the cases where tracks close to each other in the field of view of at least one sensor are associated to multiple tracks of another sensor. Finally, the robustness of the methods is proved through dedicated simulations.

Authors Balzarotti, Giorgio

Type Paper for Specialistic Magazine

Media IEEE Transactions on Aerospace and Electronic Systems (Volume: 58, Issue: 6, Date of Publication: 03 May 2022)

Web site

https://ieeexplore.ieee.org/document/9767758

Anno 2022

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