OpenRadar 安装与硬件连�?

中文

OpenRadar 自己并不负责完整的板级控制链路。它的重点是�?ADC 数据做后处理，因此仍然依�?TI 的工具链去完成设备连接、配置和数据导出�?

典型工作�?

1. �?mmWave Studio 连接雷达并确认固件匹配�?
2. �?mmWave Studio 中配�?profileConfig、chirpConfig、frameConfig�?
3. 通过 DCA1000 / TSW1400 抓取 ADC 数据�?
4. �?OpenRadar �?dataloader 读取或转发数据�?
5. 将数据送入 mmwave.dsp、mmwave.tracking、mmwave.clustering 等模块�?

迁移自原文的关键建议

• 连接问题：不同固件版本会影响是否能正常连接，必要时需要替�?mmWave Studio 自带固件�?
• 配置边界：真正与硬件采集直接相关的主要是 profileConfig、chirpConfig、frameConfig；许多其他参数属于软件处理参数�?
• **采集后处�?*：OpenRadar 支持 DCA1000 的实时流与离线处理，以及 TSW1400 的离线处理�?
• **自动�?*：TI �?Lua 脚本可以自动化连接、配置、采集，OpenRadar 仓库中的 scripts 可以作为参考�?

English

OpenRadar does not replace the full TI hardware toolchain. Its main value is Python-side ADC processing, so it still depends on TI tools for device connection, configuration, and data extraction.

Typical workflow

1. Connect the radar with mmWave Studio and verify firmware compatibility.
2. Configure profileConfig, chirpConfig, and frameConfig.
3. Capture ADC data through DCA1000 or TSW1400.
4. Load or forward the data through the OpenRadar dataloader.
5. Feed the data into mmwave.dsp, mmwave.tracking, and mmwave.clustering.

Key takeaways from the original setup notes

• Connection issues: firmware mismatch is a common source of failure.
• Hardware-facing parameters: profileConfig, chirpConfig, and frameConfig define the acquisition side; many other configs only affect software processing.
• Post-acquisition support: OpenRadar supports real-time and offline DCA1000 workflows, plus offline TSW1400 parsing.
• Automation: TI Lua scripts can automate the workflow, and the repository scripts are useful references.