关键词: 目标识别, 稀缺数据集, 贝叶斯网络（BN）参数学习, 凸优化

Abstract: Aiming at the problem of Bayesian networks modeling for target recognition with the relative scarce characteristic data, the method of the parameters estimation on the BN model is studied by fusing the scarce data set with the qualitative expert knowledge. Based on this, a target recognition algorithm is proposed. With known BN structure, the algorithm transforms the qualitative expert acknowledge into the BN conditional probability constraint set, and the convex optimization algorithm completes the estimation of BN target recognition model parameters. In the experimental study, the effectiveness of CSDE algorithm is verified by classical BN parameter modeling problem. Furthermore, the modeling and recognition experiments are carried out for the target recognition in real scarce sample dataset. The experimental results show that the proposed algorithm can solve the problem of target recognition parameters modeling better even when the modeling data set is scarce.

Key words: target recognition, scarce data set, Bayesian Network（BN） parameter learning, convex optimization