|
|
|
| Uncertainty Quantification Approach for Aerial Target Recognition Based on Hierarchical Bayesian Models |
| MA Yonglin1， LI Hao2， XIONG Wei3， LI Lingzhi2， TANG Jingmian2 |
| 1. No. 32006 Unit of PLA， Beijing 100081， China； 2. Air Force Early Warning Academy， Wuhan 430019，
Hubei， China； 3. Naval Aviation University， Yantai 264001， Shandong， China |
|
|
|
|
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.
|
|
Received: 01 December 2025
Published: 11 March 2026
|
|
|
|
|
|
| [1] |
SU Yalin, JIANG Guotao, ZHANG Tao, MA Jin, WEI Feiming, YU Wenxian. HRRP Data Augmentation Based on Conditional Diffusion Model[J]. Air & Space Defense, 2026, 9(1): 91-97. |
| [2] |
SHEN Tong, CHEN Jingxian, ZHONG Ping. Robustness of Radar Intelligent Recognition Models Under Adversarial Samples Attacks[J]. Air & Space Defense, 2026, 9(1): 46-51. |
| [3] |
CONG Xiaoyu, YANG Jiayi, SHAN Shichen, ZUO Qian. Research on Target Recognition Method Based on Multi-Source Information Fusion[J]. Air & Space Defense, 2026, 9(1): 12-19. |
| [4] |
LUO Zhijun, WANG Jianrui, YIN Jiawei. A Survey of Task-Driven Intelligent Target Recognition Methods in Complex Battlefield Environments[J]. Air & Space Defense, 2026, 9(1): 1-11. |
| [5] |
REN Haohao, CUI Shan, JIANG Xinyu, LIANG Shuyi, ZHOU Yun. Occluded SAR Target Recognition Based on Multi-View Mutual Learning Network[J]. Air & Space Defense, 2025, 8(6): 25-34. |
| [6] |
XU Qiang, MA Yuehua, XU Ke, PAN Jun. A Review on Intelligent Radar Target Recognition Methods[J]. Air & Space Defense, 2025, 8(5): 1-9. |
| [7] |
ZHANG Yanhe, ZANG Yuejin, CHEN Bo, XU Mingsheng. Radar HRRP Target Recognition Algorithm Based on Variational Auto-encoder with Disentangled Representation[J]. Air & Space Defense, 2022, 5(2): 87-93. |
| [8] |
WANG Jun, WANG Sai, REN Yuming, CHEN Dehong, CUI Shan, WEI Shaoming. Combining Denoising with Super Resolution for Target Detection and Recognition of SAR Image Based on Deep Learning[J]. Air & Space Defense, 2020, 3(3): 24-30. |
| [9] |
ZHOU Beibei, LIU Jue. Application of Intelligent Technology in Precision Strike System[J]. Air & Space Defense, 2019, 2(3): 77-83. |
|
|
|
|
2026, Vol. 9 
