Hybrid sparse subarray design for four-dimensional imaging radar

Abstract

Two-dimensional DOA estimation is challenging as the computational and hardware complexity could scale as the square as compared to that of one-dimensional problem. The proposed scheme relies on designing antenna locations and also involves a mix of subarray and digital beamforming to lower the overall system performance and cost by reducing the costly transceiver chains. This framework proposes a two-step solution which first isolates a target to a given range doppler bin and elevation angle by linear receive subarray in the elevation direction. However, the elevation estimate is relatively coarse which is further refined along with a high-resolution estimate of azimuth angle. This is achieved by processing the received data from a 2D sparse antenna array, which are systematically chosen to maximize the resolution in both directions. The compressive sensing algorithm is applied to the 2D sparse received array data which exploits the sparse representation of the underlying signal support. The propose approach successfully pairs the correct elevation and azimuth angles for multiple targets. The methodology is effective for a case of single data snapshot and algorithm performance scale well with the availability of multiple data snapshots. It is noted that the proposed methodology allows to further increase the system resolution when data is processed with MIMO virtual array processing.