## 3 Adaptive Array Antenna Controls with Image Recognitions  
The AAA is a smart signal processing method to control the radio directions by the differential of the signal shifts on the multiple antenna elements [13]. Moreover, the beam-forming vectors are generally  
Figure 2: Target connectivity of the proposed methods.  
determined by the MMSE (minimum mean square error) or CMA (constant modulus algorithm) [5]. However, a previous paper [5] pointed out that the convergence of the calculation might take longer periods such as a couple of minutes with the general methods, and it is supposed that the general AAA system is not proper for V2V communication. Further, another paper [14] mentioned that there is another approach to estimate the beam-forming vector by the optimization algorithm such as the LMS (least mean square) or the RLS (recursive least square), but it explained that the complexity of these calculations might be a problem.  
Therefore, first of all, this paper proposed the AAA system with machine learning-based image recognitions in the road surveillance system. In the system, the pictorial image is captured by the camera on the vehicles, and the target vehicle images are recognized with the machine learning algorism. Then, since the assumed road surveillance system needs rapid beam-forming controls, the distance and the angle of the target vehicles are calculated as shown in Figure 3.  
In the left of Figure 3, the captured images are firstly used for the estimations of the distance of the vehicle by Formula (1).  
\[d_{1}=d_{2}\sqrt{\frac{S_{2}}{S_{1}}}. \tag{1}\]  
Here, the total pixel area of the field vehicle is \(S_{1}\), and the total pixels in the captured image are \(S_{2}\). The distance of the actual field stands for \(d_{1}\), and the distance of the captured image's pixel is \(d_{2}\). For example, if the relations of total pixels \(S_{2}\) and the distance \(\mathrm{d}_{2}\) are previously measured by the certain camera, the distance for the target vehicles \(\mathrm{d}_{1}\) is approximately calculated by the ratio of a square root of \(S_{2}\) and \(S_{2}\), as shown in Figure 1. Next, the angle \(\theta\) is estimated by Formula (2), as in the right figure.  
\[\theta=\tan^{-1}\frac{x_{1}}{d_{1}}=\tan^{-1}\frac{x_{2}}{d_{2}} \tag{2}\]  
Although the general estimation from the captured vehicle images uses the differences of the two cameras or the marking points such as the centerline on the road, the proposed calculation methods realize simpler calculations for the rapid beam-forming.  
Then, adjustments of the beam-forming angle we made using the Kalman filter algorithm. The Kalman filter is widely known as the estimation algorithm that is used for the prediction of the weather forecast or autonomous driving systems. The calculation process mainly consists of two phases for the estimations. One is the time update phase, and it updates the current time and the predictions of the observed values. Another process is the measurement update phase, and it updates the observed values and the adjustments [8; 11]. In the proposed system, as mentioned in the previous paper [11], the calculated angle and distance from the captured image continuously used for the observed values in the Kalman filter process in OpenCV's API (Application Programming Interface) [15], and the output adjustment values were used for the angle controls of the beam-forming. Therefore, even if the target  
Figure 3: Parameters of the proposed calculations for the distance and the angle of the target vehicle.  
vehicles are out of sight in the system because of the obstacles, it is considered that the adjustments of the beam-forming proceed in order to keep the stable connectivity in the system.