Compressed-Domain Detection and Estimation for Colocated MIMO Radar

• Published in: IEEE transactions on aerospace and electronic systems Vol. 56; no. 6; pp. 4504 - 4518
• Main Authors: Tohidi, Ehsan, Hariri, Alireza, Behroozi, Hamid, Nayebi, Mohammad Mahdi, Leus, Geert, Petropulu, Athina P.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Capon beamformer; Clutter; clutter suppression; colocated multiple-input multiple-output (MIMO) radar; Complexity; compressed-domain signal processing; Data compression; Domains; Estimation; Hypothesis testing; MIMO (control systems); MIMO radar; Object detection; Radar; Radar antennas; Receivers; Reduction; Signal processing; Target detection
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2020.2995528

This article proposes a compressed-domain signal processing (CSP) multiple-input multiple-output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer, which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the discretized angle space. The proposed approach enables an eightfold reduction of the sample complexity in some settings as compared to a conventional compressed sensing (CS) MIMO radar, thus enabling faster target detection. Receiver operating characteristic curves of the proposed detector are provided. Simulation results show that the proposed approach outperforms recovery-based CS algorithms.