基于分层贝叶斯模型的不确定性量化空中目标识别方法OA

This paper proposes a recognition framework based on a hierarchical Bayesian model to address the challenges associated with fragmented prior knowledge and the absence of uncertainty quantification in decision-making processes for aerial target recognition within complex electromagnetic environments.By developing a three-tiered hierarchical structure encompassing"measurement noise-individual characteristics-class commonality",the intra-class physical variability of target Radar Cross Section(RCS)and sensor random noise were explicitly modelled as probability distributions,representing a novel contribution.Posterior inference was performed using Markov Chain Monte Carlo(MCMC)methods,simultaneously outputting target-class probabilities with confidence intervals.Simulation results show that under harsh observation conditions at 5dB SNR,the recognition accuracy reaches 78%,improving by 6%to 10%over Support Vector Machine(SVM)and Naive Bayes classifiers.In small-sample scenarios(5 training samples per class),the accuracy advantage increases to approximately 13%.The 95%confidence interval coverage rate exceeds 88%,validating the effectiveness of uncertainty quantification.The proposed method provides a practical pathway to robust target recognition within complex battlefield environments characterized by"small-sample+high-noise"conditions.