Adaptive Target Detection with Polarimetric FDA-MIMO Radar

• Published in: IEEE transactions on aerospace and electronic systems Vol. 59; no. 3; pp. 1 - 16
• Main Authors: Lan, Lan, Rosamilia, Massimo, Aubry, Augusto, Maio, Antonio De, Liao, Guisheng, Xu, Jingwei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; array manifold linearization; Constant false alarm rate; coordinate descent algorithm; Covariance matrix; gradient projection method; Hypotheses; Likelihood ratio; Optimization; Polarimetric FDA-MIMO radar; Polarimetry; Radar arrays; Radar detection; S matrix theory; Target detection
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2022.3210887

The problem of adaptive radar detection with a polarimetric Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar is addressed in this paper. At the design stage, the target detection problem is formulated as a composite hypothesis test, with the unknowns given by the target angle, incremental range (target displacement with respect to the center of the occupied range cell), and scattering matrix, as well as the interference covariance matrix. The formulated detection problem is handled by resorting to sub-optimal design strategies based on the Generalized Likelihood Ratio (GLR) criterion. The resulting detectors demand, under the <inline-formula><tex-math notation="LaTeX">H_\mathrm{{1}}</tex-math></inline-formula> hypothesis, the solution of a box-constrained optimization problem for which several iterative techniques, i.e., the Linearized Array Manifold (LAM), the Gradient Projection Method (GPM), and the Coordinate Descent (CD) algorithms, are exploited. At the analysis stage, the performance of the proposed architectures, which ensure the bounded CFAR property, is evaluated via Monte Carlo simulations and compared with the benchmarks in both white and colored disturbance.