Kalman filtering with real-time applications
Kalman filtering with real-time applications
Optimal Control of Stochastic Systems
Optimal Control of Stochastic Systems
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
The cougar approach to in-network query processing in sensor networks
ACM SIGMOD Record
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The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. There are various multisensor data fusion approaches, of which Kalman filtering is one of the most significant. Methods for Kalman filter based data fusion include measurement fusion and state fusion. This paper gives a simple a review of fusion and state fusion, and secondly proposes new integrated method of state fusion based on fusion procedures at the prediction and update level. To illustrate application, a simple example is performed to evaluate the proposed method.