Tiled Beamspace MVDR for 1024-element Wideband Radar

TL;DR

This work presents 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.

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.

Figures (7)

• Figure 1: Tiled UPA geometry illustrating the $T_z \times T_x$ tile partitioning, the per-tile subarray structure with $N_z \times N_x$ elements, and the definitions of elevation $(\theta)$ and azimuth $(\varphi)$ angles corresponding to the target location $(x_t, y_t, z_t)$.
• Figure 2: Coordinated tiled beamspace MVDR processing for wideband radar signals. After channelization, each tile performs 2D spatial FFT projection and AoA-dependent beamspace windowing to produce $W \times 1$ reduced-dimension vectors per target. The $T$ tile outputs are concatenated into a global $TW \times 1$ vector that feeds a centralized MVDR beamformer, followed by synthesis of a wideband signal for standard range-Doppler processing.
• Figure 3: Simulated environment with airborne targets with ground- and sea-based interferers. Targets $i$ and $j$ are in Easy and Difficult modes, respectively.
• Figure 4: Range and velocity estimation errors for Scenario A1.
• Figure 5: Range and velocity estimation errors for Scenario E2.