  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.
