For the current smart high-speed scene, the millimeter-wave radar needs to give the lane number of the vehicle while outputting the lane-level target track information. Since the millimeter-wave radar cannot identify the lane line information, it is difficult to achieve the lane estimation without the help of other sensors. This paper proposes a method to quickly estimate the lane numbers of vehicles in different high-speed scenarios with the help of high-precision maps. First, it unifies the map and vehicle track coordinate system, converts the point set of lane line in WGS-84 format to the Cartesian coordinate system with the radar as the origin after coordinate transformation. Then, through Lagrange interpolation, the X value corresponding to the Y value of the vehicle on the left and right side of the lane line is obtained. Finally, the lane to which the vehicle belongs is estimated by the ratio method. The actual measurement results show that the method can quickly and accurately estimate the lane number of the vehicle in various high-speed scenarios without the help of other sensors.
An improved CenterNet is proposed for signal recognition with time-frequency image input. The signal is transformed into time-frequency image by short-time Fourier transform, hence, the signal recognition is transformed into investigating the object detection problem in the field of image detection. Then, the advanced achievements of image detection are adopted to enhance the performance of signal recognition. Here, an improved CenterNet-based object detection network, which demonstrates great advantages in detection speed, is proposed. The results show that the proposed method identifies the signal modulation format with high speed. After training and testing on the self-collected data set with 6 types and 7800 samples, the mean average precision achieves 98.38% and the frame per second reaches 21.4. Compared with the original CenterNet, the detection speed increases more than 4 times while only reducing recognition accuracy by 0.3%, where this algorithm gives a promising way for applications of real-time signal recognition.
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