Impulse Radio Ultra-Wideband (IR-UWB) is a wireless carrier communication technology using nanosecond non-sinusoidal narrow pulses to transmit data. Therefore, the IR-UWB signal has a high resolution in the time domain and is suitable for high-precision positioning or sensing systems in IIoT scenarios. This thesis designs and implements a high-precision positioning system and a contactless sensing system based on the high temporal resolution characteristics of IR-UWB technology. The feasibility of the two applications in the IIoT is evaluated, which provides a reference for human-machine-thing positioning and human-machine interaction sensing technology in large smart factories. By analyzing the commonly used positioning algorithms in IR-UWB systems, this thesis designs an IRUWB relative positioning system based on the time of flight algorithm. The system uses the IR-UWB transceiver modules to obtain the distance data and calculates the relative position between the two individuals through the proposed relative positioning algorithm. An improved algorithm is proposed to simplify the system hardware, reducing the three serial port modules used in the positioning system to one. Based on the time of flight algorithm, this thesis also implements a contactless gesture sensing system with IR-UWB. The IR-UWB signal is sparsified by downsampling, and then the feature information of the signal is obtained by level-crossing sampling. Finally, a spiking neural network is used as the recognition algorithm to classify hand gestures
