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韩崇昭

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期刊论文

Optimal Linear Estimation Fusion-Part I: Unified Fusion Rules

韩崇昭X. Rong Li Senior Member IEEE Yunmin Zhu Jie Wang and Chongzhao Han

IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 49, NO.9, SEPTEMBER 2003,-0001,():

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摘要/描述

This paper deals with data (or information) fusion for the purpose of estimation. Three estimation fusion architectures are considered: centralized, distributed, and hybrid. A unified linear model and a general framework for these three architectures are established. Optimal fusion rules based on the best linear unbiased estimation (BLUE), the weighted least squares (WLS), and their generalized versions are presented for cases with complete, incomplete, or no prior information. These rules are more general and flexible, and have wider applicability than previous results. For example, they are in a unified form that is optimal for all of the three fusion architectures with arbitrary correlation of local estimates or observation errors across sensors or across time. They are also in explicit forms convenient for implementation. The optimal fusion rules presented are not limited to linear data models. Illustrative numerical results are provided to verify the fusion rules and demonstrate how these fusion rules can be used in cases with complete, incomplete, or no prior information.

版权说明:以下全部内容由韩崇昭上传于   2005年01月19日 17时51分29秒,版权归本人所有。

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