A Fusion Localization Algorithm with Adaptive Kalman Gain for Port Container Seamless Positioning

Abstract

BeiDou Navigation Satellite System (BDS) has provided high-precision, reliable positioning, navigation, and timing services anywhere in the world since the end of 2020. However, the positioning performance of BDS does not behave well like the other global navigation satellite systems (GNSS) in the transition areas indoor and outdoor, particularly in container ports, because the navigation signals are blocked and reflected by containers. In order to solve the above problem, as the pulse radio ultra-wideband (UWB) has the characteristics of high positioning accuracy, high multipath resolution, large bandwidth, and high communication rate, it is used as the Beidou auxiliary system, and the two are combined. In addition, the positioning algorithm greatly affects BDS/UWB integrated positioning performance. The single Newton iterative least square (LS) positioning algorithm does not take into account the continuity of position in time, leading to disordered positioning results and large positioning errors. For obtaining high positioning accuracy, the Kalman filter (KF) is introduced to process the output results of the LS algorithm, and a fusion localization algorithm of the Newton iterative least square and Kalman filter (LS-KF) with adaptive Kalman gain is proposed in this paper. The fusion algorithm can solve effectively the problem that the Kalman gain approaches a stable state after multiple observation epochs, resulting in a constant ratio between the predicted and the measured states. Besides, the algorithm can be used to assess the confidence of each measurement state, determine the Kalman gain adaptive adjustment factor according to the obtained confidence, and further adjust the Kalman gain through the adaptive factor. Theoretical analysis and simulation results show that the proposed algorithm can improve the overall positioning accuracy and anti-interference ability.