{"ID":2832306,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06536","arxiv_id":"2512.06536","title":"Tiled Beamspace MVDR for 1024-element Wideband Radar","abstract":"We present a tiled architecture for computationally efficient digital beamforming for wideband massive MIMO radar, using beamspace dimension reduction for each tile, and coordinated training of reduced-dimension MVDR beamformers across tiles. We illustrate the efficacy of our approach for a setting in which a 1024-element airborne radar platform beamforms towards airborne targets while suppressing strong interference from ground transmitters. The array is organized into eight 128-element tiles, each a 2D array with 4 (vertical) x 32 (horizontal) elements. Each tile applies a 2D spatial DFT to achieve energy concentration in beamspace, and a 1D temporal FFT to channelize the wideband signal into subbands for which narrowband array models apply. A small tile-level beamspace window is selected for each target (depending on its angle of arrival) in each subband, and coordinated training across tiles is used to compute reduced-dimension MVDR beamformers per-target, per-subband. While full-dimensional MVDR processing is infeasible for the system under consideration, we show that our proposed approach significantly outperforms beamspace MVDR beamforming for a single 128-element tile, where we set the dimensions of the spatial filter (and hence the complexity of MVDR training) to be equal in both systems.","short_abstract":"We present a tiled architecture for computationally efficient digital beamforming for wideband massive MIMO radar, using beamspace dimension reduction for each tile, and coordinated training of reduced-dimension MVDR beamformers across tiles. We illustrate the efficacy of our approach for a setting in which a 1024-elem...","url_abs":"https://arxiv.org/abs/2512.06536","url_pdf":"https://arxiv.org/pdf/2512.06536v2","authors":"[\"Oveys Delafrooz Noroozi\",\"Jiyoon Han\",\"Wei Tang\",\"Zhengya Zhang\",\"Upamanyu Madhow\"]","published":"2025-12-06T19:04:21Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}