GLRT-Based Adaptive Target Detection in FDA-MIMO Radar

• Published in: IEEE transactions on aerospace and electronic systems Vol. 57; no. 1; pp. 597 - 613
• Main Authors: Lan, Lan, Marino, Angela, Aubry, Augusto, De Maio, Antonio, Liao, Guisheng, Xu, Jingwei, Zhang, Yuhong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer architecture; Detectors; Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar; generalized likelihood ratio test (GLRT); Interference; Likelihood ratio; maximum likelihood (ML); Maximum likelihood estimates; Maximum likelihood estimation; Newton methods; Newton-based refinement; Object detection; Optimization; Radar arrays; Radar detection; Receivers; Semidefinite programming; semidefinite programming (SDP); Target detection; trigonometric polynomial functions
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2020.3028485

This article deals with the problem of adaptive target detection in the presence of homogeneous Gaussian interference with frequency diverse array multiple-input multiple-output radar. Adaptive detectors are devised according to the generalized likelihood ratio test criterion, where the position of the target within each range cell is assumed unknown. To obtain the maximum likelihood estimate of the target incremental range under the H_1 hypothesis, three different optimization strategies are pursued. They are, respectively, based on semidefinite programming, discrete grid search, and Newton method. At the analysis stage, a detection performance comparison is carried on among the new proposed adaptive detectors, benchmark, and mismatched receivers. Numerical results corroborate the effectiveness of the developed receivers.