基于轻量级自适应空间滤波与特征探测的毫米波雷达抗干扰与点云提纯流水线 / A Lightweight Adaptive Spatial Filtering and Feature Detection Pipeline for MM-Wave Radar Interference Mitigation and Point Cloud Purification

寻求早期种子轮或天使轮融资
投资与战略联盟主要联系邮箱:yuelucn@hotmail.com

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摘要 / Abstract

中文摘要
车载毫米波雷达在真实道路场景中极易受到多径杂波与同频混沌干扰的影响,导致点云输出中存在大量伪目标与高虚警率,进而引发自动驾驶系统的“幽灵刹车”问题。传统的全矩阵自适应信号处理算法因密集的复数矩阵求逆而带来巨大的算力开销,难以在嵌入式SoC芯片上实时落地。针对这一痛点,本文提出了一种基于轻量级特征探测与空间自适应级联滤波的雷达数据处理流水线。本流水线通过一阶标量指标动态识别空间异常区域,实施自适应微观质量修正与二维波峰群聚恒虚警检测。在公开的真实车载 77GHz 雷达长时程数据集上的实验表明,所提算法在开阔试验场和复杂城市道路场景下,分别实现了 60.42% 和 81.69% 的平均空间噪底清洗率,且最终点云密度平稳收敛于每帧 21.2 个和 43.7 个目标,有效抑制了 90% 以上的环境虚警,且算力开销极低,具备极高的商业部署价值。

English Abstract
Automotive millimeter-wave radars are highly susceptible to multi-path clutter and mutual interference in real-world driving environments, resulting in corrupted point clouds with high false-alarm rates, which directly triggers the critical phantom braking issue in autonomous vehicles. Traditional dense adaptive beamforming algorithms heavily rely on dense complex matrix inversions, inducing prohibitive computational overhead that hampers real-time deployment on embedded automotive SoCs. To address this bottleneck, this paper proposes a lightweight radar signal processing pipeline powered by dynamic feature detection and adaptive spatial cascaded filtering. By evaluating a first-order scalar metric, the pipeline dynamically flags spatial anomalies and applies localized magnitude correction, followed by an advanced 2D peak-grouping CFAR detector. Empirical validations on a publicly available real-world 77GHz automotive radar dataset demonstrate that the proposed framework achieves average spatial noise mitigation ratios of 60.42% and 81.69% under open-campus and complex urban on-road scenarios, respectively. Meanwhile, the output point cloud density tightly converges to 21.2 and 43.7 targets per frame, successfully suppressing over 90% of environmental false alarms with a significantly reduced computing footprint, showcasing substantial commercial deployment viability.

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商业与创投合作声明 / Funding & Commercial Partnership Overview

知识产权策略:源自状态关系熵动力学的核心算法框架和参数集作为核心商业秘密严格保密。为了优化产品上市速度和团队敏捷度,暂不启动专利申请。

投资与融资咨询:作者目前是一名独立研究员,正在寻求早期种子轮、天使轮融资或战略风险投资支持,以将该零硬件成本雷达提纯框架商业化为可扩展的雷达固件或芯片标准。

黑盒数据验证:我们欢迎来自潜在风投机构或汽车零部件一级供应商提供的、包含高杂波的原始点npy雷达诊断数据集。我们将通过压力盲测验证本算法超过 90% 的虚警消除效率。