空天防御2026，Vol.9Issue(1)：20-27,8.
基于分层贝叶斯模型的不确定性量化空中目标识别方法
Uncertainty Quantification Approach for Aerial Target Recognition Based on Hierarchical Bayesian Models
摘要
Abstract
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.关键词
目标识别/分层贝叶斯/不确定性量化/部分池化/马尔可夫链蒙特卡罗方法Key words
target recognition/hierarchical Bayesian/uncertainty quantification/partial pooling/Markov Chain Monte Carlo(MCMC)methed分类
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马永林,李浩,熊伟,李灵芝,汤景棉..基于分层贝叶斯模型的不确定性量化空中目标识别方法[J].空天防御,2026,9(1):20-27,8.基金项目
国家自然科学基金资助项目(61502522) （61502522）
国家社科基金资助项目(2022-SKJJ-B-056) （2022-SKJJ-B-056）
