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Adaptive Detection With Bayesian Framework for FDA-MIMO Radar
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DOI：10.1109/LGRS.2021.3123654.png)
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In this letter, we present an adaptive Bayesian detection framework for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. The targets are detected in Gaussian clutter with unknown but stochastic covariance matrix. We designed two detectors in the Bayesian framework, namely, Bayesian Rao (BRao) detector and Bayesian Wald (BWald) detector without training data. Numerical results reveal that the proposed detectors outperform conventional non-Bayesian counterparts. It is necessary to note that the BWald detector requires higher computational complexity than the BRao detector.
Keyword:
Detectors
Radar
Radar detection
Bayes methods
Covariance matrices
Clutter
Object detection
Adaptive detection
Bayesian
frequency diverse array multiple-input multiple-output (FDA-MIMO)
Rao
Wald
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16.4
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