论文标题
基于神经网络的多目标检测在相关的重尾混乱中
Neural Network-Based Multi-Target Detection within Correlated Heavy-Tailed Clutter
论文作者
论文摘要
这项工作解决了雷达系统中范围多普勒多个目标检测的问题，在存在缓慢的相关和重尾分布式混乱的情况下。常规的目标检测算法假定高斯分布的混乱，但是在存在相关的重尾分布式混乱的情况下，它们的性能显着降解。具有重尾分布式杂波的最佳检测算法的推导在分析上是棘手的。此外，杂波分布通常是未知的。这项工作提出了一种基于深度学习的方法，用于范围多普勒域中的多个目标检测。所提出的方法基于统一的NN模型，以处理各种信噪比 - 噪声比率（SCNR）和混乱分布的时间域雷达信号，简化了检测器架构和神经网络训练程序。在使用记录的雷达回声的各种实验中评估所提出的方法的性能，并通过模拟表明，所提出的方法的表现优于常规的细胞平均恒定的虚假标准速率（CA-CFAR）（CA-CFAR），有序稳态的CFAR（OS-CFAR）（OS-CFAR）（OS-CFAR），以及在适应性的符合符合型的范围内的检测，该检测的能力（Anmf）的探测器（Anmf）的探测器（Anmf）的验证性（Anmf）的探测器，以验证范围的检测。和混乱的场景。
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.
