用状态空间模型重构雷达目标检测，兼顾精度与可解释性。
Unfolding Target Detection with State Space Model
- 将CFAR检测器展开为状态空间模型，参数可学习
- 检测率和虚警率比传统方法提升10倍
- 仅260K参数，适合真实雷达场景部署
目标检测是雷达感知中的基础任务，为后续处理提供前提。传统基于信号处理的方法（如广泛应用的CFAR）调参困难且易受环境影响；深度学习方法虽更准确鲁棒，但缺乏可解释性和物理意义。本文提出一种新方法，将CFAR检测器与状态空间模型结合，保留其处理流程的同时，将复杂配置转化为可训练参数。该方法无需人工调参即可实现高检测性能，并保持模型可解释性。我们构建了一个仅含260K参数的轻量级模型，基于FMCW雷达在真实场景中进行人体目标检测实验。结果表明，该方法在检测率和虚警率上较CFAR及其变体提升10倍。代码已开源：https://github.com/aiot-lab/NeuroDet。
原文摘要 · Abstract (English)
Target detection is a fundamental task in radar sensing, serving as the precursor to any further processing for various applications. Numerous detection algorithms have been proposed. Classical methods based on signal processing, e.g., the most widely used CFAR, are challenging to tune and sensitive to environmental conditions. Deep learning-based methods can be more accurate and robust, yet usually lack interpretability and physical relevance. In this paper, we introduce a novel method that combines signal processing and deep learning by unfolding the CFAR detector with a state space model architecture. By reserving the CFAR pipeline yet turning its sophisticated configurations into trainable parameters, our method achieves high detection performance without manual parameter tuning, while preserving model interpretability. We implement a lightweight model of only 260K parameters and conduct real-world experiments for human target detection using FMCW radars. The results highlight the remarkable performance of the proposed method, outperforming CFAR and its variants by 10X in detection rate and false alarm rate. Our code is open-sourced here: https://github.com/aiot-lab/NeuroDet.
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