Compressive Sensing to Reduce the Number of Elements in a Linear Antenna Array With a Phased Array Weather Radar

Abstract

For weather radar, although a phased array system is useful for rapid scanning, its development cost is several times higher than that of radar with a dish-type antenna, and it requires relatively more antenna elements, analog-to-digital converters, and phase shifters. To reduce antenna elements with the devices of phased array systems, array geometry in a random or triangular pattern is often used. However, relatively fewer antenna elements result in observation accuracy degradation due to the increased sidelobe level or the wider beamwidth. We employ compressive sensing (CS) processing [i.e., L1 minimization (L1) and basis pursuit denoising (BPDN)] to reduce the number of antenna elements. We conducted numerical simulations for point and distributed like targets and discussed the observation accuracy using L1 and BPDN. Compared to the estimations made using a full array antenna, where BPDN can be estimated with extremely high accuracy given a 25% reduction in antenna elements, we also applied CS to real measurement data obtained using the PAWR. The BPDN was also very effective for measurement data. The novelty of the study is highlighted in its discussion of the feasibility of applying CS in observation data with the PAWR. The study findings provide a reference for development cost reduction and the mass production of PAWR.