Radar Automated Target Recognition (RATR) for Unmanned Aerial Vehicles (UAVs) involves transmitting Electromagnetic Waves (EMWs) and performing target type recognition on the received radar echo, crucial for defense and aerospace applications. Previous studies highlighted the advantages of multistatic radar configurations over monostatic ones in RATR. However, fusion methods in multistatic radar configurations often suboptimally combine classification vectors from individual radars probabilistically. To address this, we propose a fully Bayesian RATR framework employing Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars. OBF, based on expected 0-1 loss, updates a Recursive Bayesian Classification (RBC) posterior distribution for target UAV type, conditioned on historical observations across multiple time steps. We evaluate the approach using simulated random walk trajectories for seven drones, correlating target aspect angles to Radar Cross Section (RCS) measurements in an anechoic chamber. Comparing against single radar Automated Target Recognition (ATR) systems and suboptimal fusion methods, our empirical results demonstrate that the OBF method integrated with RBC significantly enhances classification accuracy compared to other fusion methods and single radar configurations.

翻译：无人机雷达自动目标识别（RATR）涉及发射电磁波并对接收的雷达回波进行目标类型识别，这对国防与航空航天应用至关重要。已有研究强调了多基地雷达配置相对于单基地雷达在RATR中的优势。然而，多基地雷达配置中的融合方法通常以概率方式次优地组合来自各雷达的分类向量。为解决该问题，我们提出一种全贝叶斯RATR框架，采用最优贝叶斯融合（OBF）聚合来自多个雷达的分类概率向量。OBF基于期望0-1损失，在多个时间步上利用历史观测数据更新目标无人机类型的递推贝叶斯分类（RBC）后验分布。我们利用七种无人机的模拟随机游走轨迹评估该方法，将目标方位角与暗室测量的雷达散射截面（RCS）值相关联。通过与单雷达自动目标识别（ATR）系统及次优融合方法的对比，实证结果表明：集成RBC的OBF方法相较于其他融合方法与单雷达配置，能显著提升分类精度。