Abstract
When we try to make the best estimate of some quantity, the problem of combining results from different experiments is encountered. In multi-sensor data fusion, the problem is seen as combining observations provided by different sensors. Sensors provide observations and information on an unknown quantity, which can differ in precision. We propose a combined estimate that uses prior information. We consider the simpler aspects of the problem, so that two sensors provide an observation of the same quantity. The standard error of the observations is supposed to be known. The prior information is an interval that bounds the parameter of the estimate. We derive the proposed combined estimate methodology, and we show its efficiency in the minimum mean square sense. The proposed combined estimate is assessed using synthetic data, and an application is presented.
Highlights
The problem of making a combined estimate as a weighted mean with weights inversely proportional to the variance was discussed previously in [1]
The fusion operator is assessed in a second experimentation, and we demonstrate the feasibility of the proposed operator, in a third experimentation, for the fusion of data provided by a multi-sensor system
The proposed fusion operator is more accurate in the minimum mean square sense than the classical maximum likelihood (ML) and Maximum a posteriori (MAP) fusion operators
Summary
The problem of making a combined estimate as a weighted mean with weights inversely proportional to the variance was discussed previously in [1]. In the sequential Bayesian inference framework, the prior law models the information brought by the dynamic state equation of the Bayesian filter [15,16] In all those cases, the fusion operator that merges the prior information with the observations is a weighted sum. We propose a non-linear estimate that uses prior information in order to combine the observations This information is an approximate value of m, ma assumed to be distributed according to a Gaussian law of variance, σ32. In order to improve the MAP fusion operator (decrease its output variance), we propose to reduce the covariance between the random variables, Z (1) and Z (2) , by decreasing the covariance between Z (p1 ) and Z (p2 ). The proposed fusion operator is non-linear, because it needs a stage of selection
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
