We investigate the detection of weak complex-valued signals immersed in non-Gaussian, range-varying interference, with emphasis on maritime radar scenarios. The proposed methodology exploits a Complex-valued Variational AutoEncoder (CVAE) trained exclusively on clutter-plus-noise to perform Out-Of-Distribution detection. By operating directly on in-phase / quadrature samples, the CVAE preserves phase and Doppler structure and is assessed in two configurations: (i) using unprocessed range profiles and (ii) after local whitening, where per-range covariance estimates are obtained from neighboring profiles. Using extensive simulations together with real sea-clutter data from the CSIR maritime dataset, we benchmark performance against classical and adaptive detectors (MF, NMF, AMF-SCM, ANMF-SCM, ANMF-Tyler). In both configurations, the CVAE yields a higher detection probability Pd at matched false-alarm rate Pfa, with the most notable improvements observed under whitening. We further integrate the CVAE with the ANMF through a weighted log-p fusion rule at the decision level, attaining enhanced robustness in strongly non-Gaussian clutter and enabling empirically calibrated Pfa control under H0. Overall, the results demonstrate that statistical normalization combined with complex-valued generative modeling substantively improves detection in realistic sea-clutter conditions, and that the fused CVAE-ANMF scheme constitutes a competitive alternative to established model-based detectors.

翻译：本文研究非高斯、距离变化干扰中微弱复值信号的检测问题，重点关注海上雷达场景。所提方法利用仅在海杂波加噪声数据上训练的复值变分自编码器（CVAE）进行分布外检测。通过直接处理同相/正交采样数据，CVAE保留了相位和多普勒结构，并在两种配置下进行评估：（i）使用未经处理的距离剖面；（ii）在局部白化处理后，其中每个距离单元的协方差估计从相邻剖面获得。通过大量仿真结合CSIR海上数据集中的真实海杂波数据，我们将性能与经典及自适应检测器（MF、NMF、AMF-SCM、ANMF-SCM、ANMF-Tyler）进行基准比较。在两种配置中，CVAE在匹配的虚警率Pfa下均获得更高的检测概率Pd，其中白化条件下的改进最为显著。我们进一步通过决策级的加权对数概率融合规则将CVAE与ANMF集成，在强非高斯杂波中实现了更强的鲁棒性，并能在H0假设下进行经验校准的Pfa控制。总体而言，结果表明统计归一化与复值生成建模相结合，能实质性改善真实海杂波条件下的检测性能，且融合的CVAE-ANMF方案构成了对现有模型驱动检测器的有力替代。