# 毫米波雷达算法工程师

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- Company: 小米 (Stage unknown)
- Locations: 北京
- Role: Algorithm
- Apply on the official site: https://xiaomi.jobs.f.mioffice.cn/campus/position/7670923522495891722/detail

## Role description from source

1、信号处理链路搭建：负责FMCW（调频连续波）雷达中频信号处理，包括CFAR（恒虚警检测）、DOA（到达角）估计、MIMO虚拟孔径成像，输出高保真的目标点云或微多普勒特征谱；
2、行为识别算法研发：针对家居复杂环境（多径效应、家具杂波），研发基于深度学习（GCN、Transformer、CNN）或传统机器学习的行为分类器，精准识别行走、静坐、挥手、摔倒（Fall）、跌倒后无法起身（Lying Post-Fall）等关键状态；
3、物理模型与AI融合（重点）：借鉴模型驱动深度学习（Model-based Deep Learning）思想（如SubspaceNet类似架构），利用信号处理的物理先验（阵列流型、多普勒谱物理约束）引导神经网络训练，解决小样本及开集泛化难题；
4、时序行为分析与语义理解：基于连续帧的状态序列，构建行为语义理解模型，分析用户长期活动模式（作息规律、活动量），实现异常行为（如久坐、夜间离床过久）的主动预警；
5、边缘计算部署：将算法在嵌入式平台（TI/Skyrelay）进行轻量化优化与移植，确保家居产品的实时性与低功耗。
任职要求
1.硕士以上学历，雷达信号处理相关专业 有MIMO雷达/单基地/多基地雷达相关的项目课题或文章；
2.信号处理能力：精通 FMCW雷达测距/测速/测角原理，熟练掌握MIMO虚拟阵列设计与校准、DOA估计算法等；
3.点云与机器学习：熟悉点云基本处理（下采样、滤波、分割）及点云特征（例如：局部密度、速度分布、RCS）；具有点云分类/动作识别经验，可独立实现基于传统特征（SVM/随机森林）或深度学习（PointNet、Transformer）的方案等；
4. 生理信号提取：熟悉利用毫米波相位/距离单元提取微小位移，掌握呼吸/心率的周期估计方法（FFT峰值搜索、小波变换、VMD等），能应对体动干扰下的鲁棒提取；
5. 工程能力：具备 C/C++ 基础，熟练使用 Python + NumPy/SciPy/scikit-learn 或 MATLAB 进行快速原型开发；了解嵌入式雷达芯片（TI IWR/AWR系列、NXP、Acconeer等）底层数据格式与实机调试。
接口顶部原始条件（未做资格推断）
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opening_id: 1c8f57fe-c525-414e-b6be-fb8ffd26de37

`anm openings save --id 1c8f57fe-c525-414e-b6be-fb8ffd26de37`