  This work addresses the problem of range-Doppler multiple target detection in
a radar system in the presence of slow-time correlated and heavy-tailed
distributed clutter. Conventional target detection algorithms assume
Gaussian-distributed clutter, but their performance is significantly degraded
in the presence of correlated heavy-tailed distributed clutter. Derivation of
optimal detection algorithms with heavy-tailed distributed clutter is
analytically intractable. Furthermore, the clutter distribution is frequently
unknown. This work proposes a deep learning-based approach for multiple target
detection in the range-Doppler domain. The proposed approach is based on a
unified NN model to process the time-domain radar signal for a variety of
signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions,
simplifying the detector architecture and the neural network training
procedure. The performance of the proposed approach is evaluated in various
experiments using recorded radar echoes, and via simulations, it is shown that
the proposed method outperforms the conventional cell-averaging constant
false-alarm rate (CA-CFAR), the ordered-statistic CFAR (OS-CFAR), and the
adaptive normalized matched-filter (ANMF) detectors in terms of probability of
detection in the majority of tested SCNRs and clutter scenarios.
