Linearly constrained least squares approach for multisensor data fusion

Yifeng Zhou; Henry Leung

doi:10.1117/12.276122

16 June 1997 Linearly constrained least squares approach for multisensor data fusion

Yifeng Zhou, Henry Leung

Author Affiliations +

Proceedings Volume 3067, Sensor Fusion: Architectures, Algorithms, and Applications; (1997) https://doi.org/10.1117/12.276122
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

In this paper, we present a linearly constrained least squares (LCLS) algorithm for multisensor data fusion. While fusion is considered in the scope of linear combination, the objective of the LCLS algorithm is to minimize the energy of the linearly fused information based on empirical sensory information. Statistical performance analysis of the LCLS algorithm will be carried out including the consistency and asymptotic covariance of the estimates. Effectiveness of the proposed fusion algorithm will be evaluated numerically based on fusion of signals and images.

Citation Download Citation

Yifeng Zhou and Henry Leung "Linearly constrained least squares approach for multisensor data fusion", Proc. SPIE 3067, Sensor Fusion: Architectures, Algorithms, and Applications, (16 June 1997); https://doi.org/10.1117/12.276122

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