Rx Beamforming for Long Baseline Multistatic Radar Networks

Distributed phased array radar networks provide an improved target detection and parameter estimation performance due to extended spatial coverage and multiple observation perspectives. By increasing a network's baseline, these capabilities can be further enhanced. However, the spatial area that the Rx stations need to cover increases with growing baselines, requiring a high number of Rx beams. This poses a challenge, since the number of receiving channels is often limited by hardware or software. Therefore, designing the Rx beamformer is a key factor for baseline extension and network performance. In this paper, we analyze the use of two different beamformers for a bistatic network configuration: a commonly used sum beam method and a recently proposed eigenbeamformer. The numerical results demonstrate the superiority of the eigenbeamformer in parameter estimation accuracy, resource efficiency and baseline extension ability.