TY - JOUR
T1 - Parametric GLRT-Based Adaptive Target Detection for FDA-MIMO Radar in Gaussian Clutter
AU - He, Changshan
AU - Huang, Bang
AU - Wang, Jianping
AU - Liu, Lei
AU - Zhang, Running
N1 - Publisher Copyright:
© 1965-2011 IEEE.
PY - 2026
Y1 - 2026
N2 - This article addresses moving target detection with limited test and/or training samples for frequency diverse array multiple-input multiple-output radar under Gaussian environments. To overcome the performance degradation caused by limited test/training samples, the disturbance is modeled as an autoregressive process, and a parametric one-step generalized likelihood ratio test detector is developed. As the associated maximum likelihood (ML) estimation is nonlinear and computationally demanding, two asymptotic ML approaches are introduced to substantially reduce computational complexity, which are asymptotically equivalent to ML. The asymptotic detection and false alarm probabilities are derived in closed form, and numerical simulations validate that the proposed scheme greatly relaxes the requirement of the number of the test/training data and significantly improves detection performance compared to conventional methods.
AB - This article addresses moving target detection with limited test and/or training samples for frequency diverse array multiple-input multiple-output radar under Gaussian environments. To overcome the performance degradation caused by limited test/training samples, the disturbance is modeled as an autoregressive process, and a parametric one-step generalized likelihood ratio test detector is developed. As the associated maximum likelihood (ML) estimation is nonlinear and computationally demanding, two asymptotic ML approaches are introduced to substantially reduce computational complexity, which are asymptotically equivalent to ML. The asymptotic detection and false alarm probabilities are derived in closed form, and numerical simulations validate that the proposed scheme greatly relaxes the requirement of the number of the test/training data and significantly improves detection performance compared to conventional methods.
KW - Autoregressive (AR) process
KW - frequency diverse array multiple-input multiple-output (FDA-MIMO)
KW - generalized likelihood ratio test (GLRT)
KW - moving target detection
UR - https://www.scopus.com/pages/publications/105032826735
U2 - 10.1109/TAES.2026.3673642
DO - 10.1109/TAES.2026.3673642
M3 - Article
AN - SCOPUS:105032826735
SN - 0018-9251
VL - 62
SP - 7504
EP - 7519
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
ER -