## 1 Introduction  
Direction finding is used in various military and civilian fields such as wireless communications, electronic warfare, IoT, and vehicles [1; 2; 3; 4; 5]. Direction finding estimates the angle of the received signal through various processes using the amplitude, phase, or both of the received signals [6; 7]. Amplitude comparison is a straightforward method of angle estimation that uses the magnitude of the received signal. Compared to phase-based methods, this approach offers a simpler system configuration and set-up [8; 9; 10; 11]. On the other hand, MUSIC (multiple signal classification) and ESPRIT (estimation of signal parameters via rotation invariance) are typical methods of the angle of arrival estimation using phase, with high estimation accuracy [12; 13; 14; 15]. However, these direction-finding methodologies often require repeated sampling, which is suboptimal for resolving fast-moving targets, supporting large sensor arrays, and minimizing computational time and cost. Recently, direction-finding methods with deep neural networks (DNNs) have been researched. Compared to MUSIC or ESPRIT, the DNN has the advantage of not requiring complex computation and being able to implement real-time application [16; 17]. The direction finding of complex processes such as conventional MUSIC is replaced with DNN, and various studies are being conducted with the aim of simplifying the computational complexity and system [18; 19; 20; 21]. These studies are useful in areas that require complex multi-signal discrimination with improved computational complexity, but in areas such as sensors and the IoT with low complexity, simple amplitude comparison methods are suitable instead. The accuracy of DNN-based direction finding using amplitude comparison depends on the amount of training data. In order to increase the amount of training data, the antenna is mechanically rotated, or multiple antennas are used to accumulate the received power value [22; 23], as shown in Figure 1. However, these methods increase the physical size of the system. Not only the amount of training data but also the quality is important, and when comparing amplitudes, the distinction between the received power values must be clear to reduce the ambiguity of direction finding. In this paper, a direction-finding method based on a DNN with only one antenna structure with multi beams without additional multi antennas to increase the training data is proposed. In order to generate a multi-beam with a single antenna structure, the patch antenna is divided into four sectors by metal vias. Due to this structure, the patch operates in a coupled mode in the superposed form of even and odd mode, and the field distributions at the divided edge of the patch are asymmetrical, unlike conventional patches. In addition, the divided patch has a directional pattern in different directions. A single-patch multi-beam antenna generates eight beams with a combination of four excitation ports assigned to four-divided sectors of the patch. In addition, the beamwidth of the pattern is reduced to improve the quality of the training data by clarifying the amplitude difference between patterns. The shape of the electric field distribution at the edge of the patch is modified through the shape deformation of the patch, thereby reducing the beamwidth. In addition, based on an antenna with eight beams, the corresponding sectors where the transmission antenna is located are estimated. The number of sectors is 8 which is divided by 45 degrees in the horizontal plane, and after placing the transmission antenna only once in each sector, training data are generated based on the received power value. This paper is organized as follows. First, an antenna with multi-beam capability is proposed for the purpose of increasing the amount of training data for a DNN-based direction-finding system. Second, the antenna is fabricated and measured.