Millimeter wave imaging using sparse arrays

Abstract

In this paper, we develop a theoretical framework for short-range millimeter (mm) wave radar imaging using a sparse array of monostatic elements, and validate it via experiments. The framework is a significant departure from classical radar, which largely focuses on long-range settings in which targets are well modeled as point scatterers. For sparse arrays, the point scatterer target model leads to grating lobes, and our central contribution is to demonstrate that a patch-based target model, suitably optimized for the sensor and scene geometry, suppresses such grating lobes. Key results include the following: (a) Characterizing the number of degrees of freedom (DoF) as a function of geometry, and showing that spatial undersampling (number of elements smaller than DoF) leads to grating lobes with the point target model; (b) showing that spatial aggregation via a patch-based dictionary suppresses grating lobes, and that patch size can be optimized based on estimation-theoretic criteria; (c) providing examples of the application, and adaptation, of patch-based dictionaries for sparse reconstruction.