Abstract Multisensor data fusion has found widespread application in industry and commerce. 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 includes measurement fusion and state fusion. This paper gives first a simple a review of both measurement fusion and state fusion, and secondly proposes two new methods of state fusion based on fusion procedures at the prediction and update level, respectively, of the Kalman filter. The theoretical derivation for these algorithms are derived. To illustrate their application, a simple example is performed to evaluate the proposed methods and compare their performance with the conventional state fusion method and measurement fusion methods.