Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach

Radar automated target recognition (RATR) for unmanned aerial vehicles (UAVs) involves transmitting electromagnetic waves and performing target type recognition on the received radar echo, which has important applications in defense and aerospace. Previous work has demonstrated the benefits of employing multistatic radar configurations in RATR compared to monostatic radar configurations. However, multistatic radar configurations commonly use fusion methods, which combine the classification vectors of multiple individual radars suboptimally from a probabilistic perspective. To address this issue, this work leverages Bayesian analysis to provide a fully Bayesian RATR framework for UAV type classification. Specifically, we employ an optimal Bayesian fusion (OBF) method, from the Bayesian perspective of expected 0âA Â 1 loss, to formulate a posterior distribution that aggregates the classification probability vectors from multiple individual radar observations at a given time step. This OBF method is used to update a separate recursive Bayesian classification (RBC) posterior distribution on the target UAV type. The RBC posterior is conditioned on all historical observations made from multiple radars across multiple time steps. To evaluate the proposed approach, we simulate random walk trajectories for seven drones and correspond the target's aspect angles to radar cross section measurements acquired in an anechoic chamber. We then compare the performance of a single-radar automated target recognition system and suboptimal fusion methods against the OBF method. We empirically show that the OBF method, integrated with RBC, significantly outperforms other fusion methods and single-radar configuration in terms of classification accuracy.