用部分复数神经网络一次性处理雷达回波，提升密集目标检测精度。
Detecting radar targets swarms in range profiles with a partially complex-valued neural network
- 采用部分复数参数的生成式神经网络，端到端处理完整信号
- 在模拟数据上显著优于传统脉冲压缩方法，尤其在目标密集时
- 适合需要高精度雷达目标分辨的军事与自动驾驶场景
正确检测雷达目标常受杂波和波形失真挑战。多个目标间距过近时，可能被误判为单一目标或相互影响检测阈值。这种负面影响与雷达的距离分辨率及自适应阈值密切相关。本文针对包含多目标、不同间距和失真回波的雷达距离剖面检测问题，提出一种部分复数值神经网络作为自适应距离剖面处理方法。通过仿真生成数据集，实验对比了传统的脉冲压缩方法与一种仅部分参数为复数的简单神经网络。脉冲压缩逐脉冲处理，而所提神经网络为生成式架构，一次性处理整个接收信号以生成完整检测剖面。
原文摘要 · Abstract (English)
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.
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