Combination Weight Assignment Based on Rough Set and Information Entropy for Inertial Measurement Unit Evaluation

Inertial measurement unit (IMU) evaluation, which plays an important role in the selection, storage scheme design, maintenance plan formulation, etc. of IMU, often uses the multi-attribute decision-making (MADM) method and considers multidimensional information to produce a reliable result. To address the issues of bias and one-sidedness of a single weighting method, a combination weighting method based on a rough set and information entropy is raised in this paper. This proposed method, integrating the statistical characteristics of data, information features, and decision risk that are necessary factors for data-driven weight assignment, constructs a feasible set of weight assignment methods. Based on the information entropy, the importance of each method in the constructed set can be calculated. Subsequently, the comprehensive weights under the feasible set can be yielded with the help of the rough set theory-based redundancy elimination method. Finally, the feasibility and effectiveness of the proposed methods are verified by a practical engineering case, i.e., the accurate evaluation of IMU.