Optimal Linear Estimation and Data Fusion
نویسندگان
چکیده
منابع مشابه
Optimal Linear Estimation Fusion — Part VI: Sensor Data Compression
Abstract – In many engineering applications, estimation accuracy can be improved by data from distributed sensors. Due to limited communication bandwidth and limited processing capability at the fusion center, it is crucial to compress these data for the final estimation at the fusion center. One way of accomplishing this is to reduce the dimension of the data with minimum or no loss of informa...
متن کاملOptimal Linear Estimation Fusion—Part V: Relationships
In this paper, we continue our study of optimal linear estimation fusion in a unified, general, and systematic setting. We clarify relationships among various BLUE and WLS fusion rules with complete, incomplete, and no prior information presented in Part I before; and we quantify the effect of prior information and data on fusion performance, including conditions under which prior information o...
متن کاملOptimal Linear Estimation Fusion—Part I: Unified Fusion Rules
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...
متن کاملUnified Optimal Linear Estimation Fusion— Part II: Discussions and Examples
Several unified optimal estimation/track fusion rules in the sense of best linear unbiased estimation (BLUE) and weighted least squares (WLS) have been presented in Part I [6] for centralized, distributed, and hybrid fusion architectures. This paper discusses their pros and cons, verifies these rules, and demonstrate via simulation examples how these fusion rules can be used in cases with eithe...
متن کاملOptimal Linear Estimation Fusion— Part VII: Dynamic Systems
In this paper, we first present a general data model for discretized asynchronous multisensor systems and show that errors in the data model are correlated across sensors and with the state. This coupling renders most existing “optimal” linear fusion rules suboptimal. While our fusion rules of Part I are valid and optimal for this general model, we propose a general, exact technique to decouple...
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ژورنال
عنوان ژورنال: IEEE Transactions on Automatic Control
سال: 2006
ISSN: 0018-9286
DOI: 10.1109/tac.2006.872768