Error Analysis of Kalman Filter Applied to Phased Array Antenna Alignment

Abstract

With growing traffic needs, it has become an inevitable trend to apply information, communication, and control to the field of transportation. In real-time communication process, the coordination between satellites and logistics trucks requires precise position information for phased array antenna alignment. However, in mountain areas and forests with weak GPS signals, the information provided by GPS often has coordinate deviations caused by environmental and measurement noise. Therefore, it is difficult to provide accurate location information for phased array antenna alignment. Considering the above problems, this paper firstly compares the mean square error of the Kalman filter algorithm under the constant acceleration(CA) motion model and the Singer motion model, and analyze their respective adaptation environments. Then a Kalman filter is applied to a phased-array antenna alignment. This method mainly uses the latitude and longitude coordinate information to predict trajectory, and analyzes the off-axis angle error and the phase error in the antenna alignment. The results show that the coordinate error fluctuation amplitude of this algorithm is low, and the converge time is short. After being applied to the antenna alignment, it effectively reduces the off-axis angle error and the phase error. It is indicated that the application of Kalman filter algorithm can control these two kinds of errors within a range, which has little impact on the selection of the antenna array.