Indoor Tracking Using Extended Kalman Filter Algorithm Based on Dual Model

With the emergence of the Internet of Things (IoT), indoor positioning services have garnered increasing attention. When it comes to target tracking, the uncertainty surrounding velocity poses a challenge in obtaining the initial state, and the setting of this initial state significantly impacts early tracking performance. To address this issue, this paper proposes an extended Kalman filter (EKF) algorithm based on a dual model, aiming to mitigate the influence of this problem. Although the tracking performance of the without velocity constrained (WVC)-EKF model falls short compared to that of the velocity constraint (VC)-EKF model, the WVC-EKF model eliminates the need to grapple with the difficulty of setting the initial velocity. Consequently, the tracking process benefits from the combined strengths of both models. Firstly, the early tracking is accomplished using the WVC-EKF model. Subsequently, the WVC-EKF model serves as an auxiliary component to assist the VC-EKF model in establishing the initial state. Finally, the VC-EKF model takes charge of the subsequent tracking. In this paper, ray-tracing software was employed for simulation purposes, and the obtained results demonstrate that the proposed algorithm enhances tracking accuracy.