A Comprehensive Decision-Making Approach Based on Hierarchical Attribute Model for Information Fusion Algorithms’ Performance Evaluation

Abstract

Aiming at the problem of fusion algorithm performance evaluation in multiradar information fusion system, firstly the hierarchical attribute model of track relevance performance evaluation model is established based on the structural model and functional model and quantization methods of evaluation indicators are given; secondly a combination weighting method is proposed to determine the weights of evaluation indicators, in which the objective and subjective weights are separately determined by criteria importance through intercriteria correlation (CRITIC) and trapezoidal fuzzy scale analytic hierarchy process (AHP), and then experience factor is introduced to obtain the combination weight; at last the improved technique for order preference by similarity to ideal solution (TOPSIS) replacing Euclidean distance with Kullback-Leibler divergence (KLD) is used to sort the weighted indicator value of the evaluation object. An example is given to illustrate the correctness and feasibility of the proposed method.