Correctly detecting radar targets is usually challenged by clutter and waveform distortion. An additional difficulty stems from the relative proximity of several targets, the latter being perceived as a single target in the worst case, or influencing each other's detection thresholds. The negative impact of targets proximity notably depends on the range resolution defined by the radar parameters and the adaptive threshold adopted. This paper addresses the matter of targets detection in radar range profiles containing multiple targets with varying proximity and distorted echoes. Inspired by recent contributions in the radar and signal processing literature, this work proposes partially complex-valued neural networks as an adaptive range profile processing. Simulated datasets are generated and experiments are conducted to compare a common pulse compression approach with a simple neural network partially defined by complex-valued parameters. Whereas the pulse compression processes one pulse length at a time, the neural network put forward is a generative architecture going through the entire received signal in one go to generate a complete detection profile.

翻译：雷达目标的正确检测通常受到杂波和波形失真的挑战。一个额外的困难源于多个目标的相对邻近性，在最坏情况下这些目标会被视为单个目标，或者相互影响彼此的检测阈值。目标邻近性的负面影响尤其取决于由雷达参数定义的距离分辨率以及所采用的自适应阈值。本文探讨了在包含不同邻近程度和失真回波的多个目标的雷达距离像中进行目标检测的问题。受雷达和信号处理领域近期研究成果的启发，本文提出将部分复值神经网络作为一种自适应距离像处理方法。通过生成模拟数据集并进行实验，将常见的脉冲压缩方法与一种由复值参数部分定义的简单神经网络进行比较。脉冲压缩每次仅处理一个脉冲长度，而本文提出的神经网络是一种生成式架构，能够一次性处理整个接收信号以生成完整的检测剖面。