Joint Incremental-Range, Angle, and Doppler Estimation in FDA-MIMO Radars: 3-D Decoupled Atomic Norm Minimization

• Published in: IEEE transactions on radar systems Vol. 2; pp. 583 - 593
• Main Authors: Bagheri Jazi, Mohammadreza, Karbasi, Seyed Mohammad, Babu, Prabhu
• Format: Journal Article
• Language: English
• Published: IEEE 2024
• Subjects: 3-Ddecoupled atomic norm minimization (3D-DANM); compressed sensing (CS); Cramér-Rao lower bound (CRLB); Doppler effect; Doppler radar; Estimation; frequency diverse array multiple-input-multiple-output (FDA-MIMO) radar; Frequency estimation; MIMO communication; Radar cross-sections; Vectors
• ISSN: 2832-7357; 2832-7357
• DOI: 10.1109/TRS.2024.3400978

This study deals with the problem of incremental-range, angle, and Doppler frequency estimation in frequency diverse array multiple-input-multiple-output (FDA-MIMO) radars. FDA-MIMO radars enjoy the advantage of range-angle-dependent beampattern, which can be used to resolve closely spaced targets and estimate their parameters. To this end, classical subspace methods are not efficient enough, specifically when working with a limited number of snapshots. In this article, a computationally efficient, compressed sensing (CS)-based method is proposed to jointly estimate incremental-range, angle, and Doppler frequency of the targets in FDA-MIMO radars. The proposed method is called 3-D decoupled atomic norm minimization (3D-DANM). It transforms the estimation problem into a semi-definite programming (SDP) problem. The range, angle, and Doppler frequency of the targets are extracted by the Vandermonde decomposition of the Toeplitz matrices resulting from the SDP problem. The extracted parameters are then paired using a specific approach. Based on the simulation results, the performance of the proposed method attains the Cramér-Rao lower bound (CRLB) for sufficient signal-to-noise ratio (SNR) values. Moreover, it offers notable enhancements in computational cost compared with existing atomic norm minimization (ANM)-based approaches.