Currently there exist three multisensor measurement fusion methods, namely, augmented method, pseudo-sequential filtering method, and combined measurement filtering method. Accuracy of these algorithms are compared by a covariance analysis method, and a conclusion is drawn that they can all obtain LMMSE (Linear Minimum Mean-Square Error) estimation under some assumptions. Other performance of these algorithms, such as computation cost and flexibility, is compared by Monte-Carlo simulation. Results show that augmented method based on information filter has the lowest computation cost and highest flexibility, augmented method based Kalman filter and pseudo-sequential filter have higher computation cost, and the two combined measurement filters are less flexible because they demand that sensor measurement matrixes satisfy some additive conditions. These conclusions are valuable in practical engineering applications.