Low-Complexity Joint 3D Super-Resolution Estimation of Range Velocity and Angle of Multi-Targets Based on FMCW Radar

Yingchun Li; Qi Long; Zhongjie Wu; Zhiquan Zhou

doi:10.3390/s22176474

Low-Complexity Joint 3D Super-Resolution Estimation of Range Velocity and Angle of Multi-Targets Based on FMCW Radar

Sensors (Basel). 2022 Aug 28;22(17):6474. doi: 10.3390/s22176474.

Authors

Yingchun Li¹, Qi Long¹, Zhongjie Wu², Zhiquan Zhou¹

Affiliations

¹ School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China.
² School of Electronic Science, National University of Defense Technology, Changsha 410073, China.

Abstract

Multi-dimensional parameters joint estimation of multi-targets is introduced to implement super-resolution sensing in range, velocity, azimuth angle, and elevation angle for frequency-modulated continuous waveform (FMCW) radar systems. In this paper, a low complexity joint 3D super-resolution estimation of range, velocity, and angle of multi-targets is proposed for an FMCW radar with a uniform linear array. The proposed method firstly constructs the size-reduced 3D matrix in the frequency domain for the system model of an FMCW radar system. Secondly, the size-reduced 3D matrix is established, and low complexity three-level cascaded 1D spectrum estimation implemented by applying the Lagrange multiplier method is developed. Finally, the low complexity joint 3D super-resolution algorithms are validated by numerical experiments and with a 77 GHz FMCW radar built by Texas Instruments, with the proposed algorithm achieving significant estimation performance compared to conventional algorithms.

Keywords: FMCW radar; array signal processing; super resolution.

Grants and funding

This research was funded by the National Natural Science Foundation of China grant number [62001143, 61871149, 62171150], the National Natural Science Foundation of Shandong Province grant number [ZR2020QF006, ZR2020YQ46], the Major Scientific and technological innovation project of Shandong Province grant number [2020CXGC010705, 2021ZLGX05], and the National Natural Science Foundation of Hunan Province grant number [2022JJ40562]. And The APC was funded by [2021ZLGX05].