# A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering
###### Abstract  
Underground obstacle detection is the premise of unmanned driving. Aiming at the problem of obstacle detection in coal mines using a millimeter-wave radar, this paper proposes a filtering algorithm for invalid targets in roadways. Firstly, the target information output by millimeter-wave radar is analyzed, and the obstacle information of the underground roadway is extracted. Then, the filter algorithm is used to filter the resolved empty targets, false targets and non-potentially dangerous targets. The empty target whose target distance is zero in radar data is filtered directly. The pseudo targets generated by the radar performance or the instability of the echo signal are filtered through the radar-effective target life cycle method. The non-threatening targets beyond  
Single-sensor detection technology mainly includes radar, infrared cameras, and vision. Infrared cameras have some shortcomings, such as low resolution and short detection ranges. Visual detection technology can obtain obstacle information in a more comprehensive way [7; 8; 9]. The YOLOv4-Tiny-4S algorithm can accurately detect and identify all kinds of targets in the image, with an average detection accuracy of 95.35% and an average detection accuracy of 86.69% for small target \"gravel\" [10]. Meanwhile, the price is relatively low. However, the camera has the disadvantages of being greatly affected by illumination and dust and low reliability, and its real-time performance is limited by the computing power of airborne equipment [11]. Radar obstacle detection sensors are divided into lidar and millimeter-wave radar. Lidar sensors are commonly used in the field of urban road autonomous driving. They have a long detection range, weak interference from active signals and high accuracy, but they are affected by dust, high price and large size. Compared with lidar and vision, millimeter-wave radar has the characteristics of high reliability and active measurement under bad weather and unpredictable lighting conditions [12]. Most of the coal reserves in the world are underground, and more than 95% of coal mines in our country are well-mining mines with average depths of more than 500 m, whose operating environment has obvious particularity. The underground environment of coal mines is rich in dust and gas. Dust mainly comes from various coal dust and rock dust produced by road header mining rock, processing, transportation and charging. The total dust concentration can reach 500-600 mg/m\({}^{3}\)[13; 14], and the gas produced by coal mining is prone to ignition and explosion. The underground environment of coal mines with high dust and gas requires that the sensor must be inherently safe and explosion-proof and have certain penetration. The underground environment of coal mines is high temperature and humidity, affected by the geothermal effect, heat dissipation of the human body and mechanical and electrical equipment. Sometimes the air temperature is 35 \({}^{\circ}\)C high, and the relative humidity is close to 100% [15]. Therefore, in the underground environment of coal mines, the sensor needs to overcome the problems that are not found in the above-ground environment, such as dust, high temperatures, high air humidity, strong noise, strong vibration, strong airflow, and complex electromagnetic environments. Robots using millimeter-wave radar can be used in a large amount of dust, gas and other dangerous gases and the lack of a light, clear and externally-positioned environment to carry out rescue operations. The literature [16; 17] shows the good performance of millimeter-wave radar in the mining environment of heavy dust clouds (zero visibility). The underground environment of coal mines is dusty, extremely dark, and the air humidity is high. Compared with millimeter-wave radar, lidar has a higher resolution, but its detection range and accuracy are seriously decreased in harsh environments [18]. On the contrary, millimeter-wave radar has strong penetration, good robustness, and all-weather sensing ability.