| dc.description.abstract | With self-driving automobiles becoming more and more popular in recent years, various sensors could be found in cars such as Sonar, Vision, Lidar, and Radar Systems. These sensors are used to assist drivers. Exact measurement of distance and relative velocity of objects in front, besides, or behind the car allow the driver to perceive objects during bad visibility or objects hidden in the blind spot. If sensors do not accurately detect targets, it can pose a serious threat to driver’s safety. Compared with other sensors, radar has the advantages of not being easily affected by the environment, and being capable of measuring longer distances precisely. For automotive radar systems, Frequency Modulation Continuous Wave (FMCW) radar is generally utilized because the complexity of hardware in the signal processing part can be reduced, compared to that of pulse radar. However, there are interference effects, reflection loss and some system errors in the practical application, which could be mistaken for desired signals. How to reduce the noise and accurately position the object will be the focus of this paper. First, the linear regression method is used to distinguish the noise and the signal, and the deep neural network is used to locate the object. | en_US |