Integrating multisensor noisy and fuzzy data

Abstract

This paper discusses centralized integration of multisensor noisy and fuzzy data, which employs both Kalman filtering and fuzzy arithmetic. Due to the property of fuzzy arithmetic, fuzziness of the parameters in a system under the extended operation will unlimitedly increase and finally reach an unacceptable range. We have previously adopted a new compression technique to solve this problem. This paper extends our work on the filtering of single sensor noisy and fuzzy data to integrating multisensor noisy and fuzzy data. An example is given to illustrate the effectiveness of the algorithm presented.<>