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(EMW)并对接收到的雷达回波进行目标类型识别，这对国防和航空航天应用至关重要。以往研究突出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次优方式对单个雷达的分类向量进行概率组合。为解决此问题，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。基于期望0-1损失，OBF更新目标无人机类型的递归贝叶斯分类(RBC)后验分布，该分布以多个时间步长的历史观测为条件。我们利用七架无人机的模拟随机游走轨迹评估该方法，在微波暗室中将目标方位角与雷达散射截面(RCS)测量值相关联。与单雷达自动目标识别(ATR)系统及次优融合方法的对比表明，集成RBC的OBF方法相较于其他融合方法和单雷达配置显著提升了分类精度。