General data fusion for estimates with unknown cross covariances

Jeffrey K. Uhlmann

doi:10.1117/12.243195

14 June 1996 General data fusion for estimates with unknown cross covariances

Jeffrey K. Uhlmann

Proceedings Volume 2755, Signal Processing, Sensor Fusion, and Target Recognition V; (1996) https://doi.org/10.1117/12.243195
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

In this paper we present a new theoretic framework for combining sensor measurements, state estimates, or any similar type of quantity given only their means and covariances. The key feature of the new framework is that it permits the optimal fusion of estimates that are correlated to an unknown degree. This framework yields a new filtering paradigm that avoids all of the restrictive independence assumptions required by the standard Kalman filter, though at the cost of reduced rates of convergence for cases in which independence can be established.

Citation Download Citation

Jeffrey K. Uhlmann "General data fusion for estimates with unknown cross covariances", Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); https://doi.org/10.1117/12.243195

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available