Current status and outlook of UWB radar personnel localization for mine rescue
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摘要:
超宽带（UWB）雷达穿透能力强，分辨率高，可穿透煤岩等非磁性矿井坍塌物探测并定位后方被困人员。介绍了UWB雷达定位原理及其在矿山救援中的应用。从雷达定位方法、动静态目标定位、单多目标定位3个方面系统梳理了UWB雷达人员定位技术的研究现状。指出目前该技术在矿山救援领域应用存在的问题：① 在大厚度、非均匀、不连续介质环境中定位误差较大，有效探测距离有限。② 非视距环境下，雷达回波信号较弱且杂波干扰显著，导致微动目标探测定位精度低，动态目标实时定位误差大。③ 多目标信号相互干扰和遮挡效应影响定位精度。对未来面向矿山救援的UWB雷达人员定位技术研究趋势作出展望：① 通过构建跨模态信息融合模型、开发高适应性信息处理方法等优化UWB雷达定位系统，提升系统对矿井灾后环境的适应性。② 改进动静态目标定位算法，结合贝叶斯网络或深度信念网络融合静态和动态目标特征，构建基于目标状态切换的综合定位模型，提升对动静目标综合定位的适用性。③ 改进UWB雷达回波处理算法，结合自适应波束成形技术、多输入多输出技术及优化的K−means++或熵分析分层算法，有效区分多目标位置信息，并通过大量模拟实验检验其在复杂环境中的适应性和可靠性。
Abstract:Ultra-Wide Band (UWB) radar exhibits strong penetration capability and high resolution, enabling the detection and localization of trapped personnel behind coal-rock collapses in mine disasters. This paper introduces the principles of UWB radar localization and its applications in mine rescue operations. The UWB radar personnel localization technologies are systematically reviewed from three perspectives: radar localization methods, static/dynamic target localization, and single/multi-target localization. Key challenges in mine rescue scenarios are identified: ① significant localization errors and limited effective detection range in thick, heterogeneous, and discontinuous media; ② weakened radar echoes and severe clutter interference under Non-Line-of-Sight (NLOS) conditions, leading to low-precision micro-motion target detection and large real-time errors for dynamic targets; ③ signal interference and occlusion effects among multiple targets degrading localization accuracy. Future research directions of UWB radar personnel localization technology for mine rescue operations are proposed: ① optimizing the UWB radar localization system by constructing cross-modal information fusion models and developing highly adaptive signal processing methods to enhance the system's adaptability to post-mining disaster environments; ② improving the applicability of combined static and dynamic target localization by developing hybrid localization algorithms that integrate Bayesian networks or deep belief networks to fuse static and dynamic target features and establishing state-switching-based comprehensive models; ③ improving UWB radar echo processing algorithms, combining adaptive beamforming technology, Multiple Input Multiple Output (MIMO) technology, and optimized K-means++ or entropy-based hierarchical analysis algorithms, effectively distinguishing multi-target position information, and validating their adaptability and reliability in complex environments through extensive simulation experiments.
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图 2 UWB雷达相位法测角原理
Figure 2. Phase-difference-based angle estimation in UWB radar systems
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图 3 UWB雷达定位技术的矿山救援应用场景
Figure 3. Application of UWB radar positioning technology in mine rescue scenario
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