Accurate Ultra-Wideband Array Radar Imaging Using Four-Dimensional Unitary ESPRIT

Abstract

Adaptive signal processing techniques can estimate the directions of arrival (DOAs), ranges, and radial velocities of radar targets with high resolution when applied in the space, frequency, and time domains, respectively. However, the performance of these techniques is limited when they are applied separately in each domain. Recently, adaptive signal processing algorithms based on high-dimensional signal subspaces have been studied extensively. We apply a four-dimensional unitary estimation of signal parameters via rotational invariant techniques (ESPRIT) algorithm for the simultaneous estimation of the DOAs, ranges, and Doppler velocities of multiple targets in an ultra-wideband radar imaging setting and show the effectiveness of the high-dimensional ESPRIT for the near-field imaging of distributed moving targets. The four-dimensional ESPRIT algorithm is demonstrated to be able to separate targets moving in close proximity, whereas the two- and three-dimensional ESPRIT algorithms fail to separate these targets accurately because of their limited resolutions. The application of the high-dimensional ESPRIT to near-field radar imaging covers a wide range of applications that require the measurement of multiple moving targets in close proximity. An example of such an application is radar-based human monitoring. Therefore, the superior resolution of the high-dimensional ESPRIT has the potential to improve the performance of various real-world security and healthcare systems.