Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by a multistatic radar network with localization errors of 25.4 cm and 19.7 cm, a detection rate of the two targets of 88.75%, and no false alarm trajectory.

使用超宽带 (UWB) 雷达节点网络对移动的非协作人员和物体进行精确定位和跟踪已被证明是一种实用且有效的方法。在 UWB 雷达传感器网络 (RSN) 中，强杂波、弱目标回波和密集目标的存在是实现令人满意的跟踪性能的障碍。使用跟踪前检测（TBD）方法，联合处理每个节点在一个时间段内获得的波形。在生成所有可能的候选轨迹时利用测量之间的空间信息和时间关系，并且仅选择最佳轨迹作为结果。数值模拟和两个实验结果证实了为 UWB RSN 开发的 TBD 技术的有效性，两者都是用实际的 UWB 信号进行的。在第一个实验中，人类目标被单基地雷达网络跟踪，平均定位误差为 41.9 cm，在杂乱的室外环境中没有误报轨迹。在第二个实验中，多基地雷达网络检测到两个目标，定位误差分别为25.4 cm和19.7 cm，两个目标的检测率为88.75%，并且没有误报轨迹。