Due to its light and weather-independent sensing, millimeter-wave (MMW) radar is essential in smart environments. Intelligent vehicle systems and industry-grade MMW radars have integrated such capabilities. Industry-grade MMW radars are expensive and hard to get for community-purpose smart environment applications. However, commercially available MMW radars have hidden underpinning challenges that need to be investigated for tasks like recognizing objects and activities, real-time person tracking, object localization, etc. Image and video data are straightforward to gather, understand, and annotate for such jobs. Image and video data are light and weather-dependent, susceptible to the occlusion effect, and present privacy problems. To eliminate dependence and ensure privacy, commercial MMW radars should be tested. MMW radar's practicality and performance in varied operating settings must be addressed before promoting it. To address the problems, we collected a dataset using Texas Instruments' Automotive mmWave Radar (AWR2944) and reported the best experimental settings for object recognition performance using different deep learning algorithms. Our extensive data gathering technique allows us to systematically explore and identify object identification task problems under cross-ambience conditions. We investigated several solutions and published detailed experimental data.

翻译：毫米波雷达凭借其对光照条件不敏感且不受天气影响的感知特性，在智能环境中不可或缺。智能车辆系统与工业级毫米波雷达已集成此类功能。然而，工业级毫米波雷达成本高昂且难以获取，难以应用于面向社区的智能环境场景。商用毫米波雷达虽易获得，但在物体识别与活动感知、实时人员追踪、目标定位等任务中仍存在亟待探究的底层技术挑战。图像与视频数据虽易于采集、理解和标注，但其感知过程依赖光照条件、易受天气影响、存在遮挡效应，且引发隐私问题。为消除上述依赖性并保障隐私安全，有必要对商用毫米波雷达进行测试验证。在推广毫米波雷达前，必须解决其在不同运行环境下的实用性与性能表现问题。针对这些挑战，我们采用德州仪器车载毫米波雷达（AWR2944）采集数据集，并报告了采用不同深度学习算法实现物体识别性能的最佳实验配置。通过系统化数据采集方法，我们得以在跨环境条件下系统探究并识别物体识别任务中的核心问题，同时研究了多种解决方案并公开了详细实验数据。