Adaptive Target Detection for FDA-MIMO Radar with Training Data in Gaussian noise
arxiv(2024)
摘要
This paper addresses the problem of detecting a moving target embedded in
Gaussian noise with an unknown covariance matrix for frequency diverse array
multiple-input multiple-output (FDA-MIMO) radar. To end it, assume that
obtaining a set of training data is available. Moreover, we propose three
adaptive detectors in accordance with the one-step generalized likelihood ratio
test (GLRT), two-step GLRT, and Rao criteria, namely OGLRT, TGLRT, and Rao. The
LH adaptive matched filter (LHAMF) detector is also introduced when decomposing
the Rao test. Next, all provided detectors have constant false alarm rate
(CFAR) properties against the covariance matrix. Besides, the closed-form
expressions for false alarm probability (PFA) and detection probability (PD)
are derived. Finally, this paper substantiates the correctness of the
aforementioned algorithms through numerical simulations.
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